Atlas Based Segmentation of the prostate in MR images (original) (raw)
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Automatic atlas-based segmentation of the prostate
2009
This paper presents a method for the automatic segmentation of the prostate from pelvic axial magnetic resonance (MR) images incorporating nonrigid registration with probabilistic atlases (PAs) as part of the 2009 MICCAI prostate segmentation challenge. This scheme was trained and evaluated on two sets of small field of view prostate images comprising (i) a set of 15 T2w Fast Spin Echo (FSE) axial MR images; and (ii) a single axial FSE axial MR image; both acquired with surface coils. Our scheme involves several steps, including (i) generation of PAs for the prostate and (ii) segmentation using nonrigid registration based propagation of the PAs. A population of preprocessed images were used to build an average shape atlas. The PAs for the prostate were generated for this atlas by propagating each subject's manual segmentations. Segmentation was performed by registering the atlas to each preprocessed image and propagating and thresholding the PAs. The automatic segmentation results were compared to the manual segmentations using the Dice Similarity Coefficient (DSC) with a median DSC for the prostate of 0.76.
2009
This paper presents a method for the automatic segmentation of the prostate from pelvic axial magnetic resonance (MR) images incorporating nonrigid registration with probabilistic atlases (PAs) as part of the 2009 MICCAI prostate segmentation challenge. This scheme was trained and evaluated on two sets of small field of view prostate images comprising (i) a set of 15 T2w Fast Spin Echo (FSE) axial MR images; and (ii) a single axial FSE axial MR image; both acquired with surface coils. Our scheme involves several steps, including (i) generation of PAs for the prostate and (ii) segmentation using nonrigid registration based propagation of the PAs. A population of preprocessed images were used to build an average shape atlas. The PAs for the prostate were generated for this atlas by propagating each subject's manual segmentations. Segmentation was performed by registering the atlas to each preprocessed image and propagating and thresholding the PAs. The automatic segmentation results were compared to the manual segmentations using the Dice Similarity Coefficient (DSC) with a median DSC for the prostate of 0.76.
Atlas-based prostate segmentation using an hybrid registration
International Journal of Computer Assisted Radiology and Surgery, 2008
Purpose: This paper presents the preliminary results of a semi-automatic method for prostate segmentation of Magnetic Resonance Images (MRI) which aims to be incorporated in a navigation system for prostate brachytherapy.
Comparison of automated segmentation techniques for magnetic resonance images of the prostate
BMC Medical Imaging
Background Contouring of anatomical regions is a crucial step in the medical workflow and is both time-consuming and prone to intra- and inter-observer variability. This study compares different strategies for automatic segmentation of the prostate in T2-weighted MRIs. Methods This study included 100 patients diagnosed with prostate adenocarcinoma who had undergone multi-parametric MRI and prostatectomy. From the T2-weighted MR images, ground truth segmentation masks were established by consensus from two expert radiologists. The prostate was then automatically contoured with six different methods: (1) a multi-atlas algorithm, (2) a proprietary algorithm in the Syngo.Via medical imaging software, and four deep learning models: (3) a V-net trained from scratch, (4) a pre-trained 2D U-net, (5) a GAN extension of the 2D U-net, and (6) a segmentation-adapted EfficientDet architecture. The resulting segmentations were compared and scored against the ground truth masks with one 70/30 and ...
Gland and Zonal Segmentation of Prostate on T2W MR Images
Journal of Digital Imaging, 2016
For many years, prostate segmentation on MR imagesconcerned only the extraction of the entire gland. Currently, in the focal treatment era, there is a continuously increasing need for the separation of the different parts of the organ. In this paper, we propose an automatic segmentation method based on the use of T2W images and atlas images to segment the prostate and to isolate the peripheral and transition zones. The algorithm consists of two stages. First, the target image is registered with each zonal atlas image then the segmentation is obtained by the application ofan evidential C-Means clustering. The method was evaluated on a representative and multi-centric images base and yielded mean Dice accuracy values of 0.81, 0.70 and 0.62 for the prostate, the transition zone and peripheral zone respectively.
This paper presents a 3D non-rigid registration algorithm between histological and MR images of the prostate with cancer. To compensate for the loss of 3D integrity in the histology sectioning process, series of 2D histological slices are first reconstructed into a 3D histological volume. After that, the 3D histology-MRI registration is obtained by maximizing a) landmark similarity and b) cancer region overlap between the two images. The former aims to capture distortions at prostate boundary and internal blob-like structures; and the latter aims to capture distortions specifically at cancer regions. In particular, landmark similarities, the former, is maximized by an annealing process, where correspondences between the automatically-detected boundary and internal landmarks are iteratively established in a fuzzy-to-deterministic fashion. Cancer region overlap, the latter, is maximized in a joint cancer segmentation and registration framework, where the two interleaved problems - segmentation and registration - inform each other in an iterative fashion. Registration accuracy is established by comparing against human-rater-defined landmarks and by comparing with other methods. The ultimate goal of this registration is to warp the histologically-defined cancer ground truth into MRI, for more thoroughly understanding MRI signal characteristics of the prostate cancerous tissue, which will promote the MRI-based prostate cancer diagnosis in the future studies.
Recent Automatic Segmentation Algorithms of MRI Prostate Regions: A Review
IEEE Acces, 2021
Worldwide incidence rate of prostate cancer has progressively increased with time especially with the increased proportion of elderly population. Early detection of prostate cancer when it is confined to the prostate gland has the best chance of successful treatment and increase in surviving rate. Prostate cancer occurrence rate varies over the three prostate regions, peripheral zone (PZ), transitional zone (TZ), and central zone (CZ) and this characteristic is one of the important considerations is development of segmentation algorithm. In fact, the occurrence rate of cancer PZ, TZ and CZ regions is respectively. at 70-80%, 10-20%, 5% or less. In general application of medical imaging, segmentation tasks can be time consuming for the expert to delineate the region of interest, especially when involving large numbers of images. In addition, the manual segmentation is subjective depending on the expert's experience. Hence, the need to develop automatic segmentation algorithms has rapidly increased along with the increased need of diagnostic tools for assisting medical practitioners, especially in the absence of radiologists. The prostate gland segmentation is challenging due to its shape variability in each zone from patient to patient and different tumor levels in each zone. This survey reviewed 22 machine learning and 88 deep learning-based segmentation of prostate MRI papers, including all MRI modalities. The review coverage includes the initial screening and imaging techniques, image pre-processing, segmentation techniques based on machine learning and deep learning techniques. Particular attention is given to different loss functions used for training segmentation based on deep learning techniques. Besides, a summary of publicly available prostate MRI image datasets is also provided. Finally, the future challenges and limitations of current deep learning-based approaches and suggestions of potential future research are also discussed. INDEX TERMS MRI, prostate cancer, deep learning, automatic algorithms, prostate gland.