Yoshito Otake | Nara Institute of Science and Technology (NAIST) (original) (raw)

Papers by Yoshito Otake

International Journal of Computer Assisted Radiology and Surgery

Purpose Artificial intelligence (AI) technologies have enabled precise three-dimensional analysis... more Purpose Artificial intelligence (AI) technologies have enabled precise three-dimensional analysis of individual muscles on computed tomography (CT) or magnetic resonance images via automatic segmentation. This study aimed to perform three-dimensional assessments of pelvic and thigh muscle atrophy and fatty degeneration in patients with unilateral hip osteoarthritis using CT and to evaluate the correlation with health-related quality of life (HRQoL). Methods The study included one man and 43 women. Six muscle groups were segmented, and the muscle atrophy ratio was calculated volumetrically. The degree of fatty degeneration was defined as the difference between the mean CT values (Hounsfield units [HU]) of the healthy and affected sides. HRQoL was evaluated using the Western Ontario and McMaster Universities Osteoarthritis (WOMAC) index and the Japanese Orthopaedic Association Hip Disease Evaluation Questionnaire (JHEQ). Results The mean muscle atrophy rate was 16.3%, and the mean deg...

ArXiv, 2019

Computer-aided surgical systems commonly use preoperative CT scans when performing pelvic osteoto... more Computer-aided surgical systems commonly use preoperative CT scans when performing pelvic osteotomies for intraoperative navigation. These systems have the potential to improve the safety and accuracy of pelvic osteotomies, however, exposing the patient to radiation is a significant drawback. In order to reduce radiation exposure, we propose a new smooth extrapolation method leveraging a partial pelvis CT and a statistical shape model (SSM) of the full pelvis in order to estimate a patient's complete pelvis. A SSM of normal, complete, female pelvis anatomy was created and evaluated from 42 subjects. A leave-one-out test was performed to characterise the inherent generalisation capability of the SSM. An additional leave-one-out test was conducted to measure performance of the smooth extrapolation method and an existing “cut-and-paste” extrapolation method. Unknown anatomy was simulated by keeping the axial slices of the patient's acetabulum intact and varying the amount of th...

Journal of Bone and Joint Surgery-british Volume, 2017

Introduction Patients with hip osteoarthritis have a substantial loss of muscular strength in the... more Introduction Patients with hip osteoarthritis have a substantial loss of muscular strength in the affected limb compared to the healthy limb preoperatively, but there is very little quantitative information available on preoperative muscle atrophy and degeneration and their influence on postoperative quality of life (QOL) and the risk of falls. The purpose of the present study were two folds; to assess muscle atrophy and degeneration of pelvis and thigh of patients with unilateral hip osteoarthritis using computed tomography (CT) and to evaluate their impacts on postoperative QOL and the risk of falls. Methods We used preoperative CT data of 20 patients who underwent primary total hip arthroplasty. The following 17 muscles were segmented with our developed semi-automated segmentation method: iliacus, gluteus maximus, gluteus medius, gluteus minimus, rectus femoris, tensor facia lata, adductors, pectinus, piriformis, obturator externus, obturator internus, semimenbranosus, semitendin...

Lecture Notes in Computer Science, 2021

This paper proposes a segmentation method of infection regions in the lung from CT volumes of COV... more This paper proposes a segmentation method of infection regions in the lung from CT volumes of COVID-19 patients. COVID-19 spread worldwide, causing many infected patients and deaths. CT imagebased diagnosis of COVID-19 can provide quick and accurate diagnosis results. An automated segmentation method of infection regions in the lung provides a quantitative criterion for diagnosis. Previous methods employ whole 2D image or 3D volume-based processes. Infection regions have a considerable variation in their sizes. Such processes easily miss small infection regions. Patch-based process is effective for segmenting small targets. However, selecting the appropriate patch size is difficult in infection region segmentation. We utilize the scale uncertainty among various receptive field sizes of a segmentation FCN to obtain infection regions. The receptive field sizes can be defined as the patch size and the resolution of volumes where patches are clipped from. This paper proposes an infection segmentation network (ISNet) that performs patchbased segmentation and a scale uncertainty-aware prediction aggregation method that refines the segmentation result. We design ISNet to segment infection regions that have various intensity values. ISNet has multiple encoding paths to process patch volumes normalized by multiple intensity ranges. We collect prediction results generated by ISNets having various receptive field sizes. Scale uncertainty among the prediction results is extracted by the prediction aggregation method. We use an aggregation FCN to generate a refined segmentation result considering scale uncertainty among the predictions. In our experiments using 199 chest CT volumes of COVID-19 cases, the prediction aggregation method improved the dice similarity score from 47.6% to 62.1%.

Medical Imaging 2021: Computer-Aided Diagnosis, 2021

This paper proposes an automated segmentation method of infection and normal regions in the lung ... more This paper proposes an automated segmentation method of infection and normal regions in the lung from CT volumes of COVID-19 patients. From December 2019, novel coronavirus disease 2019 (COVID-19) spreads over the world and giving significant impacts to our economic activities and daily lives. To diagnose the large number of infected patients, diagnosis assistance by computers is needed. Chest CT is effective for diagnosis of viral pneumonia including COVID-19. A quantitative analysis method of condition of the lung from CT volumes by computers is required for diagnosis assistance of COVID-19. This paper proposes an automated segmentation method of infection and normal regions in the lung from CT volumes using a COVID-19 segmentation fully convolutional network (FCN). In diagnosis of lung diseases including COVID-19, analysis of conditions of normal and infection regions in the lung is important. Our method recognizes and segments lung normal and infection regions in CT volumes. To segment infection regions that have various shapes and sizes, we introduced dense pooling connections and dilated convolutions in our FCN. We applied the proposed method to CT volumes of COVID-19 cases. From mild to severe cases of COVID-19, the proposed method correctly segmented normal and infection regions in the lung. Dice scores of normal and infection regions were 0.911 and 0.753, respectively.

Physics in Medicine & Biology, 2020

Accurate and consistent mental interpretation of fluoroscopy to determine the position and orient... more Accurate and consistent mental interpretation of fluoroscopy to determine the position and orientation of acetabular bone fragments in 3D space is difficult. We propose a computer assisted approach that uses a single fluoroscopic view and quickly reports the pose of an acetabular fragment without any user input or initialization. Intraoperatively, but prior to any osteotomies, two constellations of metallic ballbearings (BBs) are injected into the wing of a patient's ilium and lateral superior pubic ramus. One constellation is located on the expected acetabular fragment, and the other is located on the remaining, larger, pelvis fragment. The 3D locations of each BB are reconstructed using three fluoroscopic views and 2D/3D registrations to a preoperative CT scan of the pelvis. The relative pose of the fragment is established by estimating the movement of the two BB constellations using a single fluoroscopic view taken after osteotomy and fragment relocation. BB detection and interview correspondences are automatically computed throughout the processing pipeline. The proposed method was evaluated on a multitude of fluoroscopic images collected from six cadaveric surgeries performed bilaterally on three specimens. Mean fragment rotation error was 2.4 ± 1.0 degrees, mean translation error was 2.1 ± 0.6 mm, and mean 3D lateral center edge angle error was 1.0 ± 0.5 degrees. The average runtime of the single-view pose estimation was 0.7 ± 0.2 seconds. The proposed method demonstrates accuracy similar to other state of the art systems which require optical tracking systems or multiple-view 2D/3D registrations with manual input. The errors reported on

The Journal of Arthroplasty, 2018

Background: Laxity of soft tissues after total hip arthroplasty is considered to be a cause of ac... more Background: Laxity of soft tissues after total hip arthroplasty is considered to be a cause of accelerated wear of bearing surfaces and dislocation. The purpose of this study is to assess the contribution of the anterior and posterior capsular ligamentous complexes and the short external rotators, except the quadratus femoris, on the stability of the hip against axial traction. Methods: The study subjects comprised 7 fresh cadavers with 12 normal hip joints. In 6 hips, soft tissues surrounding the hip joint were resected in the following order to simulate the anterior approach: anterior capsule, posterior capsule, piriformis, conjoined tendon, and external obturator. In the remaining 6 hips, soft tissues were resected in the following order to simulate the posterior approach: piriformis, conjoined tendon, external obturator, posterior capsule, and anterior capsule. Soft tissue tension was measured by applying traction amounting to 250 N with joints in the neutral position. Results: The separation distance between the femoral head and acetabulum during axial leg traction significantly increased from 4.0 to 14.5 mm on average after circumferential resection of the capsule via the anterior approach. Subsequent resection of the short external rotators increased the separation distance up to 19.0 mm, but the differences did not reach statistical significance. Resection of the short external rotators via the posterior approach did not significantly increase the separation distance; it significantly increased from 6.0 to 11.4 mm after the resection of the anterior capsule and further to 20.5 mm after the resection of the posterior capsule. Conclusion: The posterior capsule, in addition to the anterior capsule, significantly contributes to hip joint stability in distraction regardless of whether the short external rotators, except the quadratus femoris, were preserved or resected.

Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, 2014

ABSTRACT

Medical Imaging 2017: Physics of Medical Imaging, 2017

We have developed a digitally synthesized patient which we call "Zach" (Zero millisecond Adjustab... more We have developed a digitally synthesized patient which we call "Zach" (Zero millisecond Adjustable Clinical Heart) phantom, which allows for an access to the ground truth and assessment of image-based cardiac functional analysis (CFA) using CT images with clinically realistic settings. The study using Zach phantom revealed a major problem with image-based CFA: "False dyssynchrony." Even though the true motion of wall segments is in synchrony, it may appear to be dyssynchrony with the reconstructed cardiac CT images. It is attributed to how cardiac images are reconstructed and how wall locations are updated over cardiac phases. The presence and the degree of false dyssynchrony may vary from scan-to-scan, which could degrade the accuracy and the repeatability (or precision) of image-based CT-CFA exams.

Medical Imaging 2017: Physics of Medical Imaging, 2017

Cardiac motion (or functional) analysis has shown promise not only for non-invasive diagnosis of ... more Cardiac motion (or functional) analysis has shown promise not only for non-invasive diagnosis of cardiovascular diseases but also for prediction of cardiac future events. Current imaging modalities has limitations that could degrade the accuracy of the analysis indices. In this paper, we present a projection-based motion estimation method for x-ray CT that estimates cardiac motion with high spatio-temporal resolution using projection data and a reference 3D volume image. The experiment using a synthesized digital phantom showed promising results for motion analysis.

The international journal of medical robotics + computer assisted surgery : MRCAS, Jan 17, 2017

Computer-assisted corrective osteotomy using a mirror image of the normal contralateral shape as ... more Computer-assisted corrective osteotomy using a mirror image of the normal contralateral shape as reference is increasingly used. Instead, we propose to use the shape predicted by statistical learning to deal with cases demonstrating bilateral abnormality, such as bilateral trauma, congenital disease, and metabolic disease. Computed tomography (CT) scans of 100 normal forearms were used in this study. The whole bone shape was predicted from its partial shape based on statistical learning of the other 99 bones. Accuracy was evaluated by average symmetric surface distance (ASD), and translational and rotational errors. ASDs for predicted shapes were 0.71-1.03 mm. Mean absolute translational and rotational errors were 0.48-1.76 mm and 0.99-6.08°, respectively. Normal bone shape was predicted with an acceptable accuracy from its partial shape using statistical learning. Predicted shape can be an alternative to a mirror image, which may enable reduced radiation exposure and examination co...

Journal of Hip Preservation Surgery, 2016

Medical Imaging 2013: Physics of Medical Imaging, 2013

Nonlinear partial volume (NLPV) effects can be significant for objects with large attenuation dif... more Nonlinear partial volume (NLPV) effects can be significant for objects with large attenuation differences and fine detail structures near the spatial resolution limits of a tomographic system. This is particularly true for small metal devices like cochlear implants. While traditional modelbased approaches might alleviate these artifacts through very fine sampling of the image volume and subsampling of rays to each detector element, such solutions can be extremely burdensome in terms of memory and computational requirements. The work presented in this paper leverages the model-based approach called "known-component reconstruction" (KCR) where prior knowledge of a surgical device is integrated into the estimation. In KCR, the parameterization of the object separates the volume into an unknown background anatomy and a known component with unknown registration. Thus, one can model projections of an implant at very high spatial resolution while limiting the spatial resolution of the anatomy-in effect, modeling NLPV effects where they are most significant. We present modifications of the KCR approach that can be used to largely eliminate NLPV artifacts, and demonstrate the efficacy of the modified technique (with improved image quality and accurate implant position estimates) for the cochlear implant imaging scenario.

Plastic and Reconstructive Surgery, 2015

Background-The aesthetic and functional outcomes surrounding Le Fort-based, face-jaw-teeth transp... more Background-The aesthetic and functional outcomes surrounding Le Fort-based, face-jaw-teeth transplantation have been suboptimal, often leading to posttransplant class II/III skeletal profiles, palatal defects, and "hybrid malocclusion." Therefore, a novel technology-real-time cephalometry-was developed to provide the surgical team instantaneous, intraoperative knowledge of three-dimensional dentoskeletal parameters. Methods-Mock face-jaw-teeth transplantation operations were performed on plastic and cadaveric human donor/recipient pairs (n = 2). Preoperatively, cephalometric landmarks were identified on donor/recipient skeletons using segmented computed tomographic scans. The computer-assisted planning and execution workstation tracked the position of the donor face-jawteeth segment in real time during the placement/inset onto recipient, reporting pertinent hybrid cephalometric parameters from any movement of donor tissue. The intraoperative data measured through real-time cephalometry were compared to posttransplant measurements for accuracy assessment. In addition, posttransplant cephalometric relationships were compared to planned outcomes to determine face-jaw-teeth transplantation success. Results-Compared with postoperative data, the real-time cephalometry-calculated intraoperative measurement errors were 1.37 ± 1.11 mm and 0.45 ± 0.28 degrees for the plastic The first two authors should be considered co-first authors.

Medical Imaging 2012: Physics of Medical Imaging, 2012

Because tomographic reconstructions are ill-conditioned, algorithms that incorporate additional k... more Because tomographic reconstructions are ill-conditioned, algorithms that incorporate additional knowledge about the imaging volume generally have improved image quality. This is particularly true when measurements are noisy or have missing data. This paper presents a general reconstruction framework for including attenuation contributions from objects known to be in the field-of-view. Components such as surgical devices and tools may be modeled explicitly as part of the attenuating volume but are inexactly known with respect to their locations poses, and possible deformations. The proposed reconstruction framework, referred to as Known-Component Reconstruction (KCR), is based on this novel parameterization of the object, a likelihood-based objective function, and alternating optimizations between registration and image parameters to jointly estimate the both the underlying attenuation and unknown registrations. A deformable KCR (dKCR) approach is introduced that adopts a control point-based warping operator to accommodate shape mismatches between the component model and the physical component, thereby allowing for a more general class of inexactly known components. The KCR and dKCR approaches are applied to low-dose cone-beam CT data with spine fixation hardware present in the imaging volume. Such data is particularly challenging due to photon starvation effects in projection data behind the metallic components. The proposed algorithms are compared with traditional filtered-backprojection and penalized-likelihood reconstructions and found to provide substantially improved image quality. Whereas traditional approaches exhibit significant artifacts that complicate detection of breaches or fractures near metal, the KCR framework tends to provide good visualization of anatomy right up to the boundary of surgical devices.

Medical Physics, 2014

Purpose: A method is presented for generating simulated low-dose cone-beam CT (CBCT) preview imag... more Purpose: A method is presented for generating simulated low-dose cone-beam CT (CBCT) preview images from which patient-and task-specific minimum-dose protocols can be confidently selected prospectively in clinical scenarios involving repeat scans. Methods: In clinical scenarios involving a series of CBCT images, the low-dose preview (LDP) method operates upon the first scan to create a projection dataset that accurately simulates the effects of dose reduction in subsequent scans by injecting noise of proper magnitude and correlation, including both quantum and electronic readout noise as important components of image noise in flat-panel detector CBCT. Experiments were conducted to validate the LDP method in both a head phantom and a cadaveric torso by performing CBCT acquisitions spanning a wide dose range (head: 0.8-13.2 mGy, body: 0.8-12.4 mGy) with a prototype mobile C-arm system. After injecting correlated noise to simulate dose reduction, the projections were reconstructed using both conventional filtered backprojection (FBP) and an iterative, model-based image reconstruction method (MBIR). The LDP images were then compared to real CBCT images in terms of noise magnitude, noise-power spectrum (NPS), spatial resolution, contrast, and artifacts. Results: For both FBP and MBIR, the LDP images exhibited accurate levels of spatial resolution and contrast that were unaffected by the correlated noise injection, as expected. Furthermore, the LDP image noise magnitude and NPS were in strong agreement with real CBCT images acquired at the corresponding, reduced dose level across the entire dose range considered. The noise magnitude agreed within 7% for both the head phantom and cadaveric torso, and the NPS showed a similar level of agreement up to the Nyquist frequency. Therefore, the LDP images were highly representative of real image quality across a broad range of dose and reconstruction methods. On the other hand, naïve injection of uncorrelated noise resulted in strong underestimation of the true noise, which would lead to overly optimistic predictions of dose reduction. Conclusions: Correlated noise injection is essential to accurate simulation of CBCT image quality at reduced dose. With the proposed LDP method, the user can prospectively select patient-specific, minimum-dose protocols (viz., acquisition technique and reconstruction method) suitable to a particular imaging task and to the user's own observer preferences for CBCT scans following the first acquisition. The method could provide dose reduction in common clinical scenarios involving multiple CBCT scans, such as image-guided surgery and radiotherapy.

Medical Physics, 2011

Purpose: We aim to develop tomographic reconstruction methods that utilize prior CT volumes to im... more Purpose: We aim to develop tomographic reconstruction methods that utilize prior CT volumes to improve image quality for sparse and undersampled datasets. The ability to handle such data has applications in region‐of‐interest scanning, tomosynthesis, and intraoperative imaging based on few view angles. While compressed sensing techniques like the PICCS algorithm are attractive, there are potential drawbacks. Such approaches often rely on pre‐registered prior images and the forward models for PICCS‐type algorithms tend to be simplified and ignore the noise model. We leverage the ability of likelihood‐based techniques to incorporate both prior images and sophisticated forward models. Methods: We have developed a framework for reconstruction of sparse tomographic data based on penalized‐likelihood estimation. The objective function for this approach is composed of a likelihood term that incorporates the forward model and noise; and a parameterized penalty term that discourages differences from the prior image. The penalty uses a compressed sensing norm and is parameterized to accommodate registration of the prior scan. An iterative algorithm is used to simultaneously solve for the tomographic volume and the registration parameters. The framework is general and can be used with both rigid and deformable registrations. Results: Reconstruction of sparse data using the proposed approach is demonstrated in cases where either rigid or deformable registration is employed in the reconstruction objective. We compare reconstruction results with traditional approaches where prior image data is not utilized. We find a substantial improvement in image quality with significant reduction in artifacts that are associated with the sparse data acquisitions. Conclusions: A new class of penalized‐likelihood estimator has been developed that simultaneously refines attenuation and registration estimates. The framework is general and supports both rigid and non‐rigid transformations of the prior imagery.Image quality and handling of artifacts using the proposed estimator with sparse data is superior to traditional approaches.

Simulation and Synthesis in Medical Imaging, 2018

CT is commonly used in orthopedic procedures. MRI is used along with CT to identify muscle struct... more CT is commonly used in orthopedic procedures. MRI is used along with CT to identify muscle structures and diagnose osteonecrosis due to its superior soft tissue contrast. However, MRI has poor contrast for bone structures. Clearly, it would be helpful if a corresponding CT were available, as bone boundaries are more clearly seen and CT has a standardized (i.e., Hounsfield) unit. Therefore, we aim at MR-to-CT synthesis. While the CycleGAN was successfully applied to unpaired CT and MR images of the head, these images do not have as much variation of intensity pairs as do images in the pelvic region due to the presence of joints and muscles. In this paper, we extended the CycleGAN approach by adding the gradient consistency loss to improve the accuracy at the boundaries. We conducted two experiments. To evaluate image synthesis, we investigated dependency of image synthesis accuracy on (1) the number of training data and (2) incorporation of the gradient consistency loss. To demonstrate the applicability of our method, we also investigated segmentation accuracy on synthesized images.

International Journal of Computer Assisted Radiology and Surgery

Purpose Artificial intelligence (AI) technologies have enabled precise three-dimensional analysis... more Purpose Artificial intelligence (AI) technologies have enabled precise three-dimensional analysis of individual muscles on computed tomography (CT) or magnetic resonance images via automatic segmentation. This study aimed to perform three-dimensional assessments of pelvic and thigh muscle atrophy and fatty degeneration in patients with unilateral hip osteoarthritis using CT and to evaluate the correlation with health-related quality of life (HRQoL). Methods The study included one man and 43 women. Six muscle groups were segmented, and the muscle atrophy ratio was calculated volumetrically. The degree of fatty degeneration was defined as the difference between the mean CT values (Hounsfield units [HU]) of the healthy and affected sides. HRQoL was evaluated using the Western Ontario and McMaster Universities Osteoarthritis (WOMAC) index and the Japanese Orthopaedic Association Hip Disease Evaluation Questionnaire (JHEQ). Results The mean muscle atrophy rate was 16.3%, and the mean deg...

ArXiv, 2019

Computer-aided surgical systems commonly use preoperative CT scans when performing pelvic osteoto... more Computer-aided surgical systems commonly use preoperative CT scans when performing pelvic osteotomies for intraoperative navigation. These systems have the potential to improve the safety and accuracy of pelvic osteotomies, however, exposing the patient to radiation is a significant drawback. In order to reduce radiation exposure, we propose a new smooth extrapolation method leveraging a partial pelvis CT and a statistical shape model (SSM) of the full pelvis in order to estimate a patient's complete pelvis. A SSM of normal, complete, female pelvis anatomy was created and evaluated from 42 subjects. A leave-one-out test was performed to characterise the inherent generalisation capability of the SSM. An additional leave-one-out test was conducted to measure performance of the smooth extrapolation method and an existing “cut-and-paste” extrapolation method. Unknown anatomy was simulated by keeping the axial slices of the patient's acetabulum intact and varying the amount of th...

Journal of Bone and Joint Surgery-british Volume, 2017

Introduction Patients with hip osteoarthritis have a substantial loss of muscular strength in the... more Introduction Patients with hip osteoarthritis have a substantial loss of muscular strength in the affected limb compared to the healthy limb preoperatively, but there is very little quantitative information available on preoperative muscle atrophy and degeneration and their influence on postoperative quality of life (QOL) and the risk of falls. The purpose of the present study were two folds; to assess muscle atrophy and degeneration of pelvis and thigh of patients with unilateral hip osteoarthritis using computed tomography (CT) and to evaluate their impacts on postoperative QOL and the risk of falls. Methods We used preoperative CT data of 20 patients who underwent primary total hip arthroplasty. The following 17 muscles were segmented with our developed semi-automated segmentation method: iliacus, gluteus maximus, gluteus medius, gluteus minimus, rectus femoris, tensor facia lata, adductors, pectinus, piriformis, obturator externus, obturator internus, semimenbranosus, semitendin...

Lecture Notes in Computer Science, 2021

This paper proposes a segmentation method of infection regions in the lung from CT volumes of COV... more This paper proposes a segmentation method of infection regions in the lung from CT volumes of COVID-19 patients. COVID-19 spread worldwide, causing many infected patients and deaths. CT imagebased diagnosis of COVID-19 can provide quick and accurate diagnosis results. An automated segmentation method of infection regions in the lung provides a quantitative criterion for diagnosis. Previous methods employ whole 2D image or 3D volume-based processes. Infection regions have a considerable variation in their sizes. Such processes easily miss small infection regions. Patch-based process is effective for segmenting small targets. However, selecting the appropriate patch size is difficult in infection region segmentation. We utilize the scale uncertainty among various receptive field sizes of a segmentation FCN to obtain infection regions. The receptive field sizes can be defined as the patch size and the resolution of volumes where patches are clipped from. This paper proposes an infection segmentation network (ISNet) that performs patchbased segmentation and a scale uncertainty-aware prediction aggregation method that refines the segmentation result. We design ISNet to segment infection regions that have various intensity values. ISNet has multiple encoding paths to process patch volumes normalized by multiple intensity ranges. We collect prediction results generated by ISNets having various receptive field sizes. Scale uncertainty among the prediction results is extracted by the prediction aggregation method. We use an aggregation FCN to generate a refined segmentation result considering scale uncertainty among the predictions. In our experiments using 199 chest CT volumes of COVID-19 cases, the prediction aggregation method improved the dice similarity score from 47.6% to 62.1%.

Medical Imaging 2021: Computer-Aided Diagnosis, 2021

This paper proposes an automated segmentation method of infection and normal regions in the lung ... more This paper proposes an automated segmentation method of infection and normal regions in the lung from CT volumes of COVID-19 patients. From December 2019, novel coronavirus disease 2019 (COVID-19) spreads over the world and giving significant impacts to our economic activities and daily lives. To diagnose the large number of infected patients, diagnosis assistance by computers is needed. Chest CT is effective for diagnosis of viral pneumonia including COVID-19. A quantitative analysis method of condition of the lung from CT volumes by computers is required for diagnosis assistance of COVID-19. This paper proposes an automated segmentation method of infection and normal regions in the lung from CT volumes using a COVID-19 segmentation fully convolutional network (FCN). In diagnosis of lung diseases including COVID-19, analysis of conditions of normal and infection regions in the lung is important. Our method recognizes and segments lung normal and infection regions in CT volumes. To segment infection regions that have various shapes and sizes, we introduced dense pooling connections and dilated convolutions in our FCN. We applied the proposed method to CT volumes of COVID-19 cases. From mild to severe cases of COVID-19, the proposed method correctly segmented normal and infection regions in the lung. Dice scores of normal and infection regions were 0.911 and 0.753, respectively.

Physics in Medicine & Biology, 2020

Accurate and consistent mental interpretation of fluoroscopy to determine the position and orient... more Accurate and consistent mental interpretation of fluoroscopy to determine the position and orientation of acetabular bone fragments in 3D space is difficult. We propose a computer assisted approach that uses a single fluoroscopic view and quickly reports the pose of an acetabular fragment without any user input or initialization. Intraoperatively, but prior to any osteotomies, two constellations of metallic ballbearings (BBs) are injected into the wing of a patient's ilium and lateral superior pubic ramus. One constellation is located on the expected acetabular fragment, and the other is located on the remaining, larger, pelvis fragment. The 3D locations of each BB are reconstructed using three fluoroscopic views and 2D/3D registrations to a preoperative CT scan of the pelvis. The relative pose of the fragment is established by estimating the movement of the two BB constellations using a single fluoroscopic view taken after osteotomy and fragment relocation. BB detection and interview correspondences are automatically computed throughout the processing pipeline. The proposed method was evaluated on a multitude of fluoroscopic images collected from six cadaveric surgeries performed bilaterally on three specimens. Mean fragment rotation error was 2.4 ± 1.0 degrees, mean translation error was 2.1 ± 0.6 mm, and mean 3D lateral center edge angle error was 1.0 ± 0.5 degrees. The average runtime of the single-view pose estimation was 0.7 ± 0.2 seconds. The proposed method demonstrates accuracy similar to other state of the art systems which require optical tracking systems or multiple-view 2D/3D registrations with manual input. The errors reported on

The Journal of Arthroplasty, 2018

Background: Laxity of soft tissues after total hip arthroplasty is considered to be a cause of ac... more Background: Laxity of soft tissues after total hip arthroplasty is considered to be a cause of accelerated wear of bearing surfaces and dislocation. The purpose of this study is to assess the contribution of the anterior and posterior capsular ligamentous complexes and the short external rotators, except the quadratus femoris, on the stability of the hip against axial traction. Methods: The study subjects comprised 7 fresh cadavers with 12 normal hip joints. In 6 hips, soft tissues surrounding the hip joint were resected in the following order to simulate the anterior approach: anterior capsule, posterior capsule, piriformis, conjoined tendon, and external obturator. In the remaining 6 hips, soft tissues were resected in the following order to simulate the posterior approach: piriformis, conjoined tendon, external obturator, posterior capsule, and anterior capsule. Soft tissue tension was measured by applying traction amounting to 250 N with joints in the neutral position. Results: The separation distance between the femoral head and acetabulum during axial leg traction significantly increased from 4.0 to 14.5 mm on average after circumferential resection of the capsule via the anterior approach. Subsequent resection of the short external rotators increased the separation distance up to 19.0 mm, but the differences did not reach statistical significance. Resection of the short external rotators via the posterior approach did not significantly increase the separation distance; it significantly increased from 6.0 to 11.4 mm after the resection of the anterior capsule and further to 20.5 mm after the resection of the posterior capsule. Conclusion: The posterior capsule, in addition to the anterior capsule, significantly contributes to hip joint stability in distraction regardless of whether the short external rotators, except the quadratus femoris, were preserved or resected.

Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, 2014

ABSTRACT

Medical Imaging 2017: Physics of Medical Imaging, 2017

We have developed a digitally synthesized patient which we call "Zach" (Zero millisecond Adjustab... more We have developed a digitally synthesized patient which we call "Zach" (Zero millisecond Adjustable Clinical Heart) phantom, which allows for an access to the ground truth and assessment of image-based cardiac functional analysis (CFA) using CT images with clinically realistic settings. The study using Zach phantom revealed a major problem with image-based CFA: "False dyssynchrony." Even though the true motion of wall segments is in synchrony, it may appear to be dyssynchrony with the reconstructed cardiac CT images. It is attributed to how cardiac images are reconstructed and how wall locations are updated over cardiac phases. The presence and the degree of false dyssynchrony may vary from scan-to-scan, which could degrade the accuracy and the repeatability (or precision) of image-based CT-CFA exams.

Medical Imaging 2017: Physics of Medical Imaging, 2017

Cardiac motion (or functional) analysis has shown promise not only for non-invasive diagnosis of ... more Cardiac motion (or functional) analysis has shown promise not only for non-invasive diagnosis of cardiovascular diseases but also for prediction of cardiac future events. Current imaging modalities has limitations that could degrade the accuracy of the analysis indices. In this paper, we present a projection-based motion estimation method for x-ray CT that estimates cardiac motion with high spatio-temporal resolution using projection data and a reference 3D volume image. The experiment using a synthesized digital phantom showed promising results for motion analysis.

The international journal of medical robotics + computer assisted surgery : MRCAS, Jan 17, 2017

Computer-assisted corrective osteotomy using a mirror image of the normal contralateral shape as ... more Computer-assisted corrective osteotomy using a mirror image of the normal contralateral shape as reference is increasingly used. Instead, we propose to use the shape predicted by statistical learning to deal with cases demonstrating bilateral abnormality, such as bilateral trauma, congenital disease, and metabolic disease. Computed tomography (CT) scans of 100 normal forearms were used in this study. The whole bone shape was predicted from its partial shape based on statistical learning of the other 99 bones. Accuracy was evaluated by average symmetric surface distance (ASD), and translational and rotational errors. ASDs for predicted shapes were 0.71-1.03 mm. Mean absolute translational and rotational errors were 0.48-1.76 mm and 0.99-6.08°, respectively. Normal bone shape was predicted with an acceptable accuracy from its partial shape using statistical learning. Predicted shape can be an alternative to a mirror image, which may enable reduced radiation exposure and examination co...

Journal of Hip Preservation Surgery, 2016

Medical Imaging 2013: Physics of Medical Imaging, 2013

Nonlinear partial volume (NLPV) effects can be significant for objects with large attenuation dif... more Nonlinear partial volume (NLPV) effects can be significant for objects with large attenuation differences and fine detail structures near the spatial resolution limits of a tomographic system. This is particularly true for small metal devices like cochlear implants. While traditional modelbased approaches might alleviate these artifacts through very fine sampling of the image volume and subsampling of rays to each detector element, such solutions can be extremely burdensome in terms of memory and computational requirements. The work presented in this paper leverages the model-based approach called "known-component reconstruction" (KCR) where prior knowledge of a surgical device is integrated into the estimation. In KCR, the parameterization of the object separates the volume into an unknown background anatomy and a known component with unknown registration. Thus, one can model projections of an implant at very high spatial resolution while limiting the spatial resolution of the anatomy-in effect, modeling NLPV effects where they are most significant. We present modifications of the KCR approach that can be used to largely eliminate NLPV artifacts, and demonstrate the efficacy of the modified technique (with improved image quality and accurate implant position estimates) for the cochlear implant imaging scenario.

Plastic and Reconstructive Surgery, 2015

Background-The aesthetic and functional outcomes surrounding Le Fort-based, face-jaw-teeth transp... more Background-The aesthetic and functional outcomes surrounding Le Fort-based, face-jaw-teeth transplantation have been suboptimal, often leading to posttransplant class II/III skeletal profiles, palatal defects, and "hybrid malocclusion." Therefore, a novel technology-real-time cephalometry-was developed to provide the surgical team instantaneous, intraoperative knowledge of three-dimensional dentoskeletal parameters. Methods-Mock face-jaw-teeth transplantation operations were performed on plastic and cadaveric human donor/recipient pairs (n = 2). Preoperatively, cephalometric landmarks were identified on donor/recipient skeletons using segmented computed tomographic scans. The computer-assisted planning and execution workstation tracked the position of the donor face-jawteeth segment in real time during the placement/inset onto recipient, reporting pertinent hybrid cephalometric parameters from any movement of donor tissue. The intraoperative data measured through real-time cephalometry were compared to posttransplant measurements for accuracy assessment. In addition, posttransplant cephalometric relationships were compared to planned outcomes to determine face-jaw-teeth transplantation success. Results-Compared with postoperative data, the real-time cephalometry-calculated intraoperative measurement errors were 1.37 ± 1.11 mm and 0.45 ± 0.28 degrees for the plastic The first two authors should be considered co-first authors.

Medical Imaging 2012: Physics of Medical Imaging, 2012

Because tomographic reconstructions are ill-conditioned, algorithms that incorporate additional k... more Because tomographic reconstructions are ill-conditioned, algorithms that incorporate additional knowledge about the imaging volume generally have improved image quality. This is particularly true when measurements are noisy or have missing data. This paper presents a general reconstruction framework for including attenuation contributions from objects known to be in the field-of-view. Components such as surgical devices and tools may be modeled explicitly as part of the attenuating volume but are inexactly known with respect to their locations poses, and possible deformations. The proposed reconstruction framework, referred to as Known-Component Reconstruction (KCR), is based on this novel parameterization of the object, a likelihood-based objective function, and alternating optimizations between registration and image parameters to jointly estimate the both the underlying attenuation and unknown registrations. A deformable KCR (dKCR) approach is introduced that adopts a control point-based warping operator to accommodate shape mismatches between the component model and the physical component, thereby allowing for a more general class of inexactly known components. The KCR and dKCR approaches are applied to low-dose cone-beam CT data with spine fixation hardware present in the imaging volume. Such data is particularly challenging due to photon starvation effects in projection data behind the metallic components. The proposed algorithms are compared with traditional filtered-backprojection and penalized-likelihood reconstructions and found to provide substantially improved image quality. Whereas traditional approaches exhibit significant artifacts that complicate detection of breaches or fractures near metal, the KCR framework tends to provide good visualization of anatomy right up to the boundary of surgical devices.

Medical Physics, 2014

Purpose: A method is presented for generating simulated low-dose cone-beam CT (CBCT) preview imag... more Purpose: A method is presented for generating simulated low-dose cone-beam CT (CBCT) preview images from which patient-and task-specific minimum-dose protocols can be confidently selected prospectively in clinical scenarios involving repeat scans. Methods: In clinical scenarios involving a series of CBCT images, the low-dose preview (LDP) method operates upon the first scan to create a projection dataset that accurately simulates the effects of dose reduction in subsequent scans by injecting noise of proper magnitude and correlation, including both quantum and electronic readout noise as important components of image noise in flat-panel detector CBCT. Experiments were conducted to validate the LDP method in both a head phantom and a cadaveric torso by performing CBCT acquisitions spanning a wide dose range (head: 0.8-13.2 mGy, body: 0.8-12.4 mGy) with a prototype mobile C-arm system. After injecting correlated noise to simulate dose reduction, the projections were reconstructed using both conventional filtered backprojection (FBP) and an iterative, model-based image reconstruction method (MBIR). The LDP images were then compared to real CBCT images in terms of noise magnitude, noise-power spectrum (NPS), spatial resolution, contrast, and artifacts. Results: For both FBP and MBIR, the LDP images exhibited accurate levels of spatial resolution and contrast that were unaffected by the correlated noise injection, as expected. Furthermore, the LDP image noise magnitude and NPS were in strong agreement with real CBCT images acquired at the corresponding, reduced dose level across the entire dose range considered. The noise magnitude agreed within 7% for both the head phantom and cadaveric torso, and the NPS showed a similar level of agreement up to the Nyquist frequency. Therefore, the LDP images were highly representative of real image quality across a broad range of dose and reconstruction methods. On the other hand, naïve injection of uncorrelated noise resulted in strong underestimation of the true noise, which would lead to overly optimistic predictions of dose reduction. Conclusions: Correlated noise injection is essential to accurate simulation of CBCT image quality at reduced dose. With the proposed LDP method, the user can prospectively select patient-specific, minimum-dose protocols (viz., acquisition technique and reconstruction method) suitable to a particular imaging task and to the user's own observer preferences for CBCT scans following the first acquisition. The method could provide dose reduction in common clinical scenarios involving multiple CBCT scans, such as image-guided surgery and radiotherapy.

Medical Physics, 2011

Purpose: We aim to develop tomographic reconstruction methods that utilize prior CT volumes to im... more Purpose: We aim to develop tomographic reconstruction methods that utilize prior CT volumes to improve image quality for sparse and undersampled datasets. The ability to handle such data has applications in region‐of‐interest scanning, tomosynthesis, and intraoperative imaging based on few view angles. While compressed sensing techniques like the PICCS algorithm are attractive, there are potential drawbacks. Such approaches often rely on pre‐registered prior images and the forward models for PICCS‐type algorithms tend to be simplified and ignore the noise model. We leverage the ability of likelihood‐based techniques to incorporate both prior images and sophisticated forward models. Methods: We have developed a framework for reconstruction of sparse tomographic data based on penalized‐likelihood estimation. The objective function for this approach is composed of a likelihood term that incorporates the forward model and noise; and a parameterized penalty term that discourages differences from the prior image. The penalty uses a compressed sensing norm and is parameterized to accommodate registration of the prior scan. An iterative algorithm is used to simultaneously solve for the tomographic volume and the registration parameters. The framework is general and can be used with both rigid and deformable registrations. Results: Reconstruction of sparse data using the proposed approach is demonstrated in cases where either rigid or deformable registration is employed in the reconstruction objective. We compare reconstruction results with traditional approaches where prior image data is not utilized. We find a substantial improvement in image quality with significant reduction in artifacts that are associated with the sparse data acquisitions. Conclusions: A new class of penalized‐likelihood estimator has been developed that simultaneously refines attenuation and registration estimates. The framework is general and supports both rigid and non‐rigid transformations of the prior imagery.Image quality and handling of artifacts using the proposed estimator with sparse data is superior to traditional approaches.

Simulation and Synthesis in Medical Imaging, 2018

CT is commonly used in orthopedic procedures. MRI is used along with CT to identify muscle struct... more CT is commonly used in orthopedic procedures. MRI is used along with CT to identify muscle structures and diagnose osteonecrosis due to its superior soft tissue contrast. However, MRI has poor contrast for bone structures. Clearly, it would be helpful if a corresponding CT were available, as bone boundaries are more clearly seen and CT has a standardized (i.e., Hounsfield) unit. Therefore, we aim at MR-to-CT synthesis. While the CycleGAN was successfully applied to unpaired CT and MR images of the head, these images do not have as much variation of intensity pairs as do images in the pelvic region due to the presence of joints and muscles. In this paper, we extended the CycleGAN approach by adding the gradient consistency loss to improve the accuracy at the boundaries. We conducted two experiments. To evaluate image synthesis, we investigated dependency of image synthesis accuracy on (1) the number of training data and (2) incorporation of the gradient consistency loss. To demonstrate the applicability of our method, we also investigated segmentation accuracy on synthesized images.