Gabriele Piantadosi | Università degli Studi di Napoli "Federico II" (original) (raw)

Papers by Gabriele Piantadosi

Journal of Imaging, 2021

The recent spread of Deep Learning (DL) in medical imaging is pushing researchers to explore its ... more The recent spread of Deep Learning (DL) in medical imaging is pushing researchers to explore its suitability for lesion segmentation in Dynamic Contrast-Enhanced Magnetic-Resonance Imaging (DCE-MRI), a complementary imaging procedure increasingly used in breast-cancer analysis. Despite some promising proposed solutions, we argue that a “naive” use of DL may have limited effectiveness as the presence of a contrast agent results in the acquisition of multimodal 4D images requiring thorough processing before training a DL model. We thus propose a pipelined approach where each stage is intended to deal with or to leverage a peculiar characteristic of breast DCE-MRI data: the use of a breast-masking pre-processing to remove non-breast tissues; the use of Three-Time-Points (3TP) slices to effectively highlight contrast agent time course; the application of a motion-correction technique to deal with patient involuntary movements; the leverage of a modified U-Net architecture tailored on th...

2018 24th International Conference on Pattern Recognition (ICPR), 2018

Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated, in recent years,... more Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) has demonstrated, in recent years, a great potential as a complementary diagnostic method for early detection and diagnosis of breast cancer. However, due to the large amount of data, DCE-MRI manual inspection is error prone and can hardly be handled without the use of a Computer Aided Diagnosis (CAD) system. In a typical CAD processing, the segmentation of the breast parenchyma is a crucial stage aimed to reduce computational effort and to increase reliability. In the last years, deep convolutional networks have outperformed the state-of-the-art in many visual tasks, such as image classification and object recognition. However, very few proposals based on a deep learning approach have been applied so far for segmentation tasks in the biomedical field. The aim of this work is to apply a suitably modified convolutional neural network for fully-automating the non-trivial breast tissues segmentation task in 3D MR data, in order to accurately segment breast parenchyma from the air and other tissues (such as chest-wall). The proposed approach has been validated over 42 DCE-MRI studies. The median segmentation accuracy and Dice similarity index were 98.93 (±0, 15) and 95.90 (±0, 74) respectively with p< 0.05, and 100% of neoplastic lesion coverage.

Reproducible Research in Pattern Recognition, 2019

Breast cancer is the most common women tumour worldwide, about 2 million new cases diagnosed each... more Breast cancer is the most common women tumour worldwide, about 2 million new cases diagnosed each year (second most common cancer overall). This disease represents about 12% of all new cancer cases and 25% of all cancers in women. Early detection of breast cancer is one of the key factors in determining the prognosis for women with malignant tumours. The standard diagnostic tool for the detection of breast cancer is x-ray mammography. The disadvantage of this method is its low specificity, especially in the case of radiographically dense breast tissue (young or under-forty women), or in the presence of scars and implants within the breast. Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has demonstrated a great potential in the screening of high-risk women for breast cancer, in staging newly diagnosed patients and in assessing therapy effects. However, due to the large amount of information, DCE-MRI manual examination is error prone and can hardly be inspected without...

In the Big-Data era, many engineering tasks have to deal with extracting valuable information fro... more In the Big-Data era, many engineering tasks have to deal with extracting valuable information from large amount of data. This is supported by different methodologies, many of which strongly rely on curve fitting (both linear and non-linear). One of the most common approach to solve this kind of problems is the use of least squares method, usually by iterative procedures that can cause slowness when applied to problems that require to repeat the fitting procedure many times. In this work we propose a method to speed-up the curve fitting evaluation by means of a Look-up Table (LuT) approach, exploiting problems resilience. The considered case study is the fitting of breast Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) data to a pharmacokinetic model, that needs to be fast for clinical usage. To validate the proposed approach, we compared our results with those obtained by using the well-known Levenberg-Marquardt algorithm (LMA). Results show that the proposed approach...

The aim of this work is to investigate the effectiveness of novel human-machine interaction parad... more The aim of this work is to investigate the effectiveness of novel human-machine interaction paradigms for eHealth applications. In particular, we propose to replace usual human-machine interaction mechanisms with an approach that leverages a chat-bot program, opportunely designed and trained in order to act and interact with patients as a human being. Moreover, we have validated the proposed interaction paradigm in a real clinical context, where the chat-bot has been employed within a medical decision support system having the goal of providing useful recommendations concerning several disease prevention pathways. More in details, the chat-bot has been realized to help patients in choosing the most proper disease prevention pathway by asking for different information (starting from a general level up to specific pathways questions) and to support the related prevention check-up and the final diagnosis. Preliminary experiments about the effectiveness of the proposed approach are repo...

Supporting the early diagnosis of skin cancer is crucial for the sake of any kind of treatment or... more Supporting the early diagnosis of skin cancer is crucial for the sake of any kind of treatment or surgery. This work proposes to improve the outcome of automatic diagnoses approaches by using an ensemble of pre-trained deep convolutional neural networks and a suitable voting strategy. Moreover, a novel patching approach has been deployed. The proposal has been fairly evaluated with the literature proposals demonstrating good preliminary results.

2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS), 2019

Nowadays, Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is increasingly succeedi... more Nowadays, Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is increasingly succeeding as a complementary methodology for breast cancer, with Computer Aided Detection/Diagnosis (CAD) systems becoming essential technological tools to provide early detection and diagnosis of tumours. Several CADs make use of machine learning, resulting in a constant design of hand-crafted features aimed at better assisting the physician. In recent years, Deep learning (DL) approaches raised in popularity in many pattern recognition tasks thanks to their ability to learn compact hierarchical features that well fit the specific task to solve. If, on one and, this characteristic suggests to explore DL suitability for biomedical image processing, on the other, it is important to take into account the physiological inheritance of the images under analysis. With this goal in mind, in this work we propose "3TP U-Net", an U-Shaped Deep Convolutional Neural Network that exploits the well-known Three Time Points approach for the lesion segmentation task. Results show that our proposal is able to outperform not only the classical (non-deep) approaches but also some very recent deep proposal, achieving a median Dice Similarity Coefficient of 61.24%.

2019 27th European Signal Processing Conference (EUSIPCO), 2019

With the increasing spread of easy and effective frameworks, in recent years Deep Learning approa... more With the increasing spread of easy and effective frameworks, in recent years Deep Learning approaches are becoming more and more used in several application fields, including computer vision (such as natural and biomedical image processing), automatic speech recognition (ASR) and time-series analysis. If, on one hand, the availability of such frameworks allows developers to use the one they feel more comfortable with, on the other, it raises questions related to the reproducibility of the designed model across different hardware and software configurations, both at training and at inference times. The reproducibility assessment is important to determine if the resulting model produces good or bad outcomes just because of luckier or blunter environmental training conditions. This is a non-trivial problem for Deep Learning based applications, not only because their training and optimization phases strongly rely on stochastic procedures, but also because of the use of some heuristic considerations (mainly speculative procedures) at training time that, although they help in reducing the required computational effort, tend to introduce non-deterministic behavior, with a direct impact on the results and on the model’s reproducibility. Usually, to face this problem, designers make use of probabilistic considerations about the distribution of data or focus their attention on very huge datasets. However, this kind of approach does not really fit some application field standards (such as medical imaging analysis with Computer-Aided Detection and Diagnosis systems – CAD) that require strong demonstrable proofs of effectiveness and repeatability of results across the population. It is our opinion that in those cases it is of crucial importance to clarify if and to what extent a Deep Learning based application is stable and repeatable as well as effective, across different environmental (hardware and software) configurations. Therefore, the aim of this work is to quantitatively analyze the reproducibility problem of Convolutional Neural Networks (CNN) based approaches for the biomedical image processing, in order to highlight the impact that a given software framework and hardware configurations might have when facing the same problem by the same means. In particular, we analyzed the problem of breast tissue segmentation in DCE-MRI by using a modified version of a 2D U-Net CNN, a very effective deep architecture for semantic segmentation, using two Deep Learning frameworks (MATLAB and TensorFlow) across different hardware configurations.

Lecture Notes in Computer Science, 2019

Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is a diagnostic method for the det... more Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is a diagnostic method for the detection and diagnosis of breast cancer. Requiring the acquisition of images before and after the injection of a paramagnetic contrast agent, it provides a large amount of data that can hardly be analyzed without the use of a Computer Aided Diagnosis (CAD) system, whose aim is to support radiologists in the interpretation of medical images. Among the major issues in developing a CAD for the breast DCE-MRI there is the lesion diagnosis, namely the classification of lesioned tissues according to the tumour aggressiveness. Several studies have been conducted so far to explore the applicability of Deep Learning (DL) approaches to the automatic breast lesions classification. However, we argue that solutions only relying on DL are not so effective since past learned experience in the radiomics field should also be kept in mind to better exploit the dynamics of contrast agent and its effect on the acquired images. To this aim, we propose an approach that exploits the well-known Three Time Points (3TP) idea to select the specific time points that best highlight the tissues under analysis. Our findings show that promising results can then be obtained by using transfer learning, resulting in an approach that is able to outperform both the classical (non-deep) and some very recent deep proposals.

Pattern Recognition. ICPR International Workshops and Challenges, 2021

With the growing number of digital medical imaging records, the need for an automatic procedure t... more With the growing number of digital medical imaging records, the need for an automatic procedure to retrieve only data of interest is of increasing importance. A Picture Archiving and Communication System (PACS) provides effective storage and retrieval based on TAGs but does not allow us for query by example. A possible solution is to use a Content-Based Image Retrieval (CBIR) system, namely a system able to retrieve images from a database based on the similarity to a given reference image. The features used to describe the images strongly affect both the performance and the applicability of CBIR to medical images, motivating for the finding of a suitable set of feature for realizing an effective CBIR based PACS. In recent years, Deep Learning (DL) approaches outperformed classical machine learning methods in many computer vision applications, thanks to their ability to learn compact hierarchical features of input data that well fit the specific task to solve. In this paper we introduce a simple yet effective modular architecture to implement a “Smart PACS”, namely a PACS exploiting a deep-based CBIR compatible with the classical Hospital Information System (HIS) infrastructure. The feature extraction relies on Convolutional Neural Networks, a DL approach commonly applied in image processing, while the image indexing and look-up are based on Apache Solr. As application case-study, we analysed the need for a physician to obtain all the images of past studies having similar traits with the patient under analysis.

Computer Analysis of Images and Patterns, 2019

Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is a diagnostic method suited for ... more Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is a diagnostic method suited for the early detection and diagnosis of cancer, involving the serial acquisition of images before and after the injection of a paramagnetic contrast agent. Dealing with long acquisition times, DCE-MRI inevitably shows noise (artefacts) in acquired images due to the patient (often involuntary) movements. As a consequence, over the years, machine learning approaches showed that some sort of motion correction technique (MCT) have to be applied in order to improve performance in tumours segmentation and classification. However, in recent times classic machine learning approaches have been outperformed by deep learning based ones, thanks to their ability to autonomously learn the best set of features for the task under analysis. This paper proposes a first investigation to understand if deep learning based approaches are more robust to the misalignment of images over time, making the registration no longer needed in this context. To this aim, we evaluated the effectiveness of a MCT both for the classification and for the segmentation of breast lesions in DCE-MRI by means of some literature proposal. Our results show that while MCTs seems to be still quite useful for the lesion segmentation task, they seem to be no longer strictly required for lesion classification one.

Image Analysis and Processing - ICIAP 2017, 2017

Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is gaining popularity as a complem... more Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is gaining popularity as a complementary diagnostic method for early detection and diagnosis of breast cancer. However, due to the large amount of data, DCE-MRI can hardly be inspected without the use of a Computer Aided Diagnosis (CAD) system. Among the major issues in developing CAD for breast DCE-MRI there is the classification of regions of interest according to their aggressiveness. For this task newer hand-crafted features are continuously proposed by domain experts. On the other hand, deep learning approaches have gained popularity in many pattern recognition tasks, being able to outperform classical machine learning techniques in different fields, by learning compact hierarchical representations of an image which well fit the specific task to solve. The aim of this work is to explore the applicability of Convolutional Neural Networks (CNN) in automatic lesion malignancy assessment for breast DCE-MRI data. Our findings show that while promising results in treating DCE-MRI can be obtained by using transfer learning, CNNs have to be carefully designed and tuned in order to outperform approaches specifically designed to exploit all the available data information.

Artificial Intelligence in Medicine, 2019

IET Computer Vision, 2018

Frontiers in oncology, 2018

Radiomics leverages existing image datasets to provide non-visible data extraction via image post... more Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contra...

2016 23rd International Conference on Pattern Recognition (ICPR), 2016

Dynamic Contrast Enhanced - Magnetic Resonance Imaging (DCE-MRI) is gaining popularity as complem... more Dynamic Contrast Enhanced - Magnetic Resonance Imaging (DCE-MRI) is gaining popularity as complementary diagnostic tool for breast cancer. In a typical Computer Aided Detection (CAD) processing, the identification and segmentation of the breast parenchyma is a crucial stage aimed to reduce computational effort and increase reliability, by reducing the number of voxels to analyse and removing foreign tissues and air. The aim of this work is to propose a fully-automated geometrical-based breast-mask extraction method in DCE-MRI, that combines three 2D Fuzzy C-Means clustering and geometrical breast anatomy characterization. In particular, seven well defined key-points have been considered in order to accurately segment breast parenchyma from air and chest-wall. The proposed approach has been validated on 30 DCE-MRI studies. The median segmentation accuracy and Dice similarity index were 97.86 (±0.49) and 92.66 (±1.48) respectively with p < 0.05, and 100% of neoplastic lesion coverage.

Journal of Reliable Intelligent Environments, 2015

Image Analysis and Processing — ICIAP 2015, 2015