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Computer Aided Diagnosis Of Prostate Cancer With Magnetic Resonance Imaging
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Book Synopsis Computer Aided Diagnosis of Prostate Cancer with Magnetic Resonance Imaging by : Pieter Christiaan Vos
Download or read book Computer Aided Diagnosis of Prostate Cancer with Magnetic Resonance Imaging written by Pieter Christiaan Vos and published by . This book was released on 2011 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Computer-aided Diagnosis for Prostate Cancer Using Multi-parametric Magnetic Resonance Imaging by : Guillaume Lemaître
Download or read book Computer-aided Diagnosis for Prostate Cancer Using Multi-parametric Magnetic Resonance Imaging written by Guillaume Lemaître and published by . This book was released on 2016 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Prostate Cancer Imaging: Computer-Aided Diagnosis, Prognosis, and Intervention by : Anant Madabhushi
Download or read book Prostate Cancer Imaging: Computer-Aided Diagnosis, Prognosis, and Intervention written by Anant Madabhushi and published by Springer Science & Business Media. This book was released on 2010-09-03 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the International Workshop on Prostate Cancer Imaging, held in conjunction with MICCAI 2010, in Beijing, China, in September 2010. The 11 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 13 submissions. The papers cover the clinical areas radiology, radiation oncology, digital pathology, and image guided intervention, addressing topics such as prostate segmentation, multi-modal prostate registration, computer-aided diagnosis and classification of prostate cancer.
Book Synopsis Prostate Cancer Imaging by : Ayman El-Baz
Download or read book Prostate Cancer Imaging written by Ayman El-Baz and published by CRC Press. This book was released on 2018-10-31 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers novel strategies and state of the art approaches for automated non-invasive systems for early prostate cancer diagnosis. Prostate cancer is the most frequently diagnosed malignancy after skin cancer and the second leading cause of cancer related male deaths in the USA after lung cancer. However, early detection of prostate cancer increases chances of patients’ survival. Generally, The CAD systems analyze the prostate images in three steps: (i) prostate segmentation; (ii) Prostate description or feature extraction; and (iii) classification of the prostate status. Explores all of the latest research and developments in state-of-the art imaging of the prostate from world class experts. Contains a comprehensive overview of 2D/3D Shape Modeling for MRI data. Presents a detailed examination of automated segmentation of the prostate in 3D imaging. Examines Computer-Aided-Diagnosis through automated techniques. There will be extensive references at the end of each chapter to enhance further study.
Book Synopsis Computer-aided Detection of Prostate Cancer Within the Peripheral Zone in T2-Weighted Magnetic Resonance Imaging by :
Download or read book Computer-aided Detection of Prostate Cancer Within the Peripheral Zone in T2-Weighted Magnetic Resonance Imaging written by and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Computer-aided Detection of Prostate Cancer Within the Peripheral Zone in T2-weighted Magnetic Resonance Imaging by : Yambu Andrik Rampun
Download or read book Computer-aided Detection of Prostate Cancer Within the Peripheral Zone in T2-weighted Magnetic Resonance Imaging written by Yambu Andrik Rampun and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Prostate Cancer Imaging. Image Analysis and Image-Guided Interventions by : Anant Madabhushi
Download or read book Prostate Cancer Imaging. Image Analysis and Image-Guided Interventions written by Anant Madabhushi and published by Springer Science & Business Media. This book was released on 2011-09 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the International Workshop on Prostate Cancer Imaging, held in conjunction with MICCAI 2011, in Toronto, Canada, in September 2011. The 15 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 19 submissions. The papers cover the clinical areas of radiology, radiation oncology, and image guided intervention, addressing topics such as prostate segmentation, multi-modal prostate registration, and computer-aided diagnosis and classification of prostate cancer.
Book Synopsis Zone-specific computer-aided diagnosis system aimed at characterizing ISUP ≥ 2 prostate cancers on multiparametric magnetic resonance images by : Théo Arber
Download or read book Zone-specific computer-aided diagnosis system aimed at characterizing ISUP ≥ 2 prostate cancers on multiparametric magnetic resonance images written by Théo Arber and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Les logiciels d'aide au diagnostic (CADx) par Imagerie par Résonnance Magnétique (IRM), ont pour objectif d'améliorer la spécificité et la variabilité inter-lecteur de cet examen. Ces progrès seraient particulièrement intéressants afin de rendre les protocoles de Surveillance Active (SA) plus fiables et sûrs. C'est pourquoi nous avons voulu tester dans une population de patients en SA, les performances de notre CADx, entraîné à diagnostiquer des lésions prostatiques ISUP ≥ 2. A partir d'une cohorte prospective incluant tous les hommes bénéficiant d'un bilan IRM- Biopsie Ciblée dans notre centre, nous avons sélectionné rétrospectivement tous les patients considérés en surveillance active et ayant passé cette IRM d'inclusion entre septembre 2015 et décembre 2016. Un uro-radiologue entraîné a ensuite contouré sur leur IRM les lésions qui ont été ciblées lors de la biopsie consécutive. Ces ROI obtenues ont été traitées par notre logiciel CAD afin d'obtenir un score de probabilité de cancer qui a été comparé aux performances du score PI-RADS et du PSA densité. La spécificité du score CAD était plus significative que celle du score PIRADS ≥ 3 (Sp= 0.60 et 0.24 respectivement, p
Book Synopsis Comprehensive Framework for Computer-aided Prostate Cancer Detection in Multi-parametric MRI by : Audrey Chung
Download or read book Comprehensive Framework for Computer-aided Prostate Cancer Detection in Multi-parametric MRI written by Audrey Chung and published by . This book was released on 2016 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: Prostate cancer is the most diagnosed form of cancer and one of the leading causes of cancer death in men, but survival rates are relatively high with sufficiently early diagnosis. The current clinical model for initial prostate cancer screening is invasive and subject to overdiagnosis. As such, the use of magnetic resonance imaging (MRI) has recently grown in popularity as a non-invasive imaging-based prostate cancer screening method. In particular, the use of high volume quantitative radiomic features extracted from multi-parametric MRI is gaining attraction for the auto-detection of prostate tumours since it provides a plethora of mineable data which can be used for both detection and prognosis of prostate cancer. Current image-based cancer detection methods, however, face notable challenges that include noise in MR images, variability between different MRI modalities, weak contrast, and non-homogeneous texture patterns, making it difficult for diagnosticians to identify tumour candidates. In this thesis, a comprehensive framework for computer-aided prostate cancer detection using multi-parametric MRI was introduced. The framework consists of two parts: i) a saliency-based method for identifying suspicious regions in multi-parametric MR prostate images based on statistical texture distinctiveness, and ii) automatic prostate tumour candidate detection using a radiomics-driven conditional random field (RD-CRF). The framework was evaluated using real clinical prostate multi-parametric MRI data from 20 patients, and both parts were compared against state-of-the-art approaches. The suspicious region detection method achieved a 1.5% increase in sensitivity, and a 10% increase in specificity and accuracy over the state-of-the-art method, indicating its potential for more visually meaningful identification of suspicious tumour regions. The RD-CRF method was shown to improve the detection of tumour candidates by mitigating sparsely distributed tumour candidates and improving the detected tumour candidates via spatial consistency and radiomic feature relationships. Thus, the developed framework shows potential for aiding medical professionals with performing more efficient and accurate computer-aided prostate cancer detection.
Book Synopsis Computer-aided Diagnosis of Prostate Cancer Using Multi-parametric MRI - Evaluation of Feature Extraction and Classification by : Sean R. Babcock
Download or read book Computer-aided Diagnosis of Prostate Cancer Using Multi-parametric MRI - Evaluation of Feature Extraction and Classification written by Sean R. Babcock and published by . This book was released on 2019 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Computer-aided Detection of Malignant Lesions in Dynamic Contrast Enhanced MRI Breast and Prostate Cancer Datasets by : Brent James Woods
Download or read book Computer-aided Detection of Malignant Lesions in Dynamic Contrast Enhanced MRI Breast and Prostate Cancer Datasets written by Brent James Woods and published by . This book was released on 2008 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is considered to have great potential in cancer diagnosis and monitoring. During the DCE-MRI procedure, repeated MRI scans are used to monitor contrast agent movement through the vascular system and into tissue. By observing the vascular permeability characteristics, radiologists can detect and classify malignant tissues. When used for diagnostic purposes, the DCE-MRI procedure often requires manual detection, classification, and marking of tumor tissues. This process can be time consuming and fatiguing especially when multiple DCE-MRI procedures must be processed to monitor the progress of a cancer therapy. Manual analysis also suffers from inter- and intra-observer variations which can lead to lesion segmentation inconsistencies. The goal of this dissertation research is to design and develop a tool to aid radiologists, researchers, and clinicians in the detection, segmentation, and analysis of malignant lesions from DCE-MRI datasets. The diagnostic tool presented in this research is model independent, speeds analysis, and provides more consistent segmentations. The approach of the project is to apply statistical 4-D image texture analysis features along with a classifier (such as a neural network) to analyze DCE-MRI datasets. Performance of the computer aided diagnosis (CAD) tool for this project is demonstrated with breast and prostate DCE-MRI data. Training methodology is reported so that extension to other types of cancers and anatomical regions is made possible. Results from the computer assisted diagnostic tool are compared with manual analysis performed by radiologists. The specific research aims of this dissertation are: a) provide a tool for quantitative and quick DCE-MRI analysis by providing radiologists a segmentation (for semi-automatic or automatic application), b) quantify inter- and intra-observer variations that occur during manual lesion segmentation and compare performance with computer-based segmentations, and c) explore the performance of the CAD system in different anatomic regions (including breast and prostate cancer datasets).
Book Synopsis Sparse Reconstruction of Compressive Sensing Magnetic Resonance Imagery Using a Cross Domain Stochastic Fully Connected Conditional Random Field Framework by : Yaguang Li
Download or read book Sparse Reconstruction of Compressive Sensing Magnetic Resonance Imagery Using a Cross Domain Stochastic Fully Connected Conditional Random Field Framework written by Yaguang Li and published by . This book was released on 2016 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: Prostate cancer is a major health care concern in our society. Early detection of prostate cancer is crucial in the successful treatment of the disease. Many current methods used in detecting prostate cancer can either be inconsistent or invasive and discomforting to the patient. Magnetic resonance imaging (MRI) has demonstrated its ability as a non-invasive and non-ionizing medical imaging modality with a lengthy acquisition time that can be used for the early diagnosis of cancer. Speeding up the MRI acquisition process can greatly increase the number of early detections for prostate cancer diagnosis. Compressive sensing has exhibited the ability to reduce the imaging time for MRI by sampling a sparse yet sufficient set of measurements. Compressive sensing strategies are usually accompanied by strong reconstruction algorithms. This work presents a comprehensive framework for a cross-domain stochastically fully connected conditional random field (CD-SFCRF) reconstruction approach to facilitate compressive sensing MRI. This approach takes into account original k-space measurements made by the MRI machine with neighborhood and spatial consistencies of the image in the spatial domain. This approach facilitates the difference in domain between MRI measurements made in the k-space, and the reconstruction results in spatial domain. An adaptive extension of the CD-SFCRF approach that takes into account regions of interest in the image and changes the CD-SFCRF neighborhood connectivity based on importance is presented and tested as well. Finally, a compensated CD-SFCRF approach that takes into account MRI machine imaging apparatus properties to correct for degradations and aberrations from the image acquisition process is presented and tested. Clinical MRI data were collected from twenty patients with ground truth data examined and con firmed by an expert radiologist with multiple years of prostate cancer diagnosis experience. Compressive sensing simulations were performed and the reconstruction results show the CD-SFCRF and extension frameworks having noticeable improvements over state of the art methods. Tissue structure and image details are well preserved while sparse sampling artifacts were reduced and eliminated. Future work on this framework include extending the current work in multiple ways. Extensions including integration into computer aided diagnosis applications as well as improving on the compressive sensing strategy.
Book Synopsis Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 by : Anne L. Martel
Download or read book Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 written by Anne L. Martel and published by Springer Nature. This book was released on 2020-10-02 with total page 867 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: machine learning methodologies Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: segmentation; shape models and landmark detection Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography
Book Synopsis PROSTATE CANCER DIAGNOSIS USING MAGNETIC RESONANCE IMAGING - A MACHINE LEARNING APPROACH by :
Download or read book PROSTATE CANCER DIAGNOSIS USING MAGNETIC RESONANCE IMAGING - A MACHINE LEARNING APPROACH written by and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Contributions to Computer-aided Diagnosis of Prostate Cancer in Histopathology by : Kien Nguyen
Download or read book Contributions to Computer-aided Diagnosis of Prostate Cancer in Histopathology written by Kien Nguyen and published by . This book was released on 2013 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Non-invasive Image Based System for Early Diagnosis of Prostate Cancer by : Ahmad Abdusalam Firjani
Download or read book A Non-invasive Image Based System for Early Diagnosis of Prostate Cancer written by Ahmad Abdusalam Firjani and published by . This book was released on 2014 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Prostate cancer is the second most fatal cancer experienced by American males. The average American male has a 16.15% chance of developing prostate cancer, which is 8.38% higher than lung cancer, the second most likely cancer. The current in-vitro techniques that are based on analyzing a patients blood and urine have several limitations concerning their accuracy. In addition, the prostate Specific Antigen (PSA) blood-based test, has a high chance of false positive diagnosis, ranging from 28%-58%. Yet, biopsy remains the gold standard for the assessment of prostate cancer, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The major limitation of the relatively small needle biopsy samples is the higher possibility of producing false positive diagnosis. Moreover, the visual inspection system (e.g., Gleason grading system) is not quantitative technique and different observers may classify a sample differently, leading to discrepancies in the diagnosis. As reported in the literature that the early detection of prostate cancer is a crucial step for decreasing prostate cancer related deaths. Thus, there is an urgent need for developing objective, non-invasive image based technology for early detection of prostate cancer. The objective of this dissertation is to develop a computer vision methodology, later translated into a clinically usable software tool, which can improve sensitivity and specificity of early prostate cancer diagnosis based on the well-known hypothesis that malignant tumors are will connected with the blood vessels than the benign tumors. Therefore, using either Diffusion Weighted Magnetic Resonance imaging (DW-MRI) or Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI), we will be able to interrelate the amount of blood in the detected prostate tumors by estimating either the Apparent Diffusion Coefficient (ADC) in the prostate with the malignancy of the prostate tumor or perfusion parameters. We intend to validate this hypothesis by demonstrating that automatic segmentation of the prostate from either DW-MRI or DCE-MRI after handling its local motion, provides discriminatory features for early prostate cancer diagnosis. The proposed CAD system consists of three majors components, the first two of which constitute new research contributions to a challenging computer vision problem. The three main components are: (1) A novel Shape-based segmentation approach to segment the prostate from either low contrast DW-MRI or DCE-MRI data; (2) A novel iso-contours-based non-rigid registration approach to ensure that we have voxel-on-voxel matches of all data which may be more difficult due to gross patient motion, transmitted respiratory effects, and intrinsic and transmitted pulsatile effects; and (3) Probabilistic models for the estimated diffusion and perfusion features for both malignant and benign tumors. Our results showed a 98% classification accuracy using Leave-One-Subject-Out (LOSO) approach based on the estimated ADC for 30 patients (12 patients diagnosed as malignant; 18 diagnosed as benign). These results show the promise of the proposed image-based diagnostic technique as a supplement to current technologies for diagnosing prostate cancer.
Book Synopsis The Role of Magnetic Resonance Imaging in the Detection, Local Staging, and Diagnosis of Prostate Cancer by : Saana Ikonen
Download or read book The Role of Magnetic Resonance Imaging in the Detection, Local Staging, and Diagnosis of Prostate Cancer written by Saana Ikonen and published by . This book was released on 2001 with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt:
