Fast Low Resource And Accurate Organ And Pan Cancer Segmentation In Abdomen Ct
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Fast Low Resource And Accurate Organ And Pan Cancer Segmentation In Abdomen Ct
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Author : Jun Ma
language : en
Publisher: Springer Nature
Release Date : 2024-07-01
Fast Low Resource And Accurate Organ And Pan Cancer Segmentation In Abdomen Ct written by Jun Ma and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-01 with Computers categories.
This book constitutes the proceedings of the MICCAI 2023 Challenge, FLARE 2023, held in Conjunction with MICCAI 2023, in Vancouver, BC, Canada, on October 8, 2023. The 27 full papers presented in this book were carefully reviewed and selected from 37 submissions. The papers present research and results for abdominal organ segmentation which has many important clinical applications, such as organ quantification, surgical planning, and disease diagnosis.
Fast Low Resource Accurate Robust Organ And Pan Cancer Segmentation
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Author : Jun Ma
language : en
Publisher: Springer Nature
Release Date : 2025-07-09
Fast Low Resource Accurate Robust Organ And Pan Cancer Segmentation written by Jun Ma and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-09 with Computers categories.
This book constitutes the proceedings of the MICCAI 2024 Challenge, FLARE 2024, held in Conjunction with MICCAI 2024, in Marrakesh, Morocco, during October 2024. The 20 full papers included in this book were carefully reviewed and selected from 24 submissions. They describe the solutions the participants found for automatic abdominal organ and pan-cancer segmentation using the official training dataset released for this pupose. This challenge focuses on both organ and pan-cancer segmentation, including three subtasks: Subtask 1: Pan-cancer segmentation in CT scans Subtask 2: Abdominal CT organ segmentation on laptop Subtask 3: Unsupervised domain adaptation for abdominal organ segmentation in MRI Scans
Fast And Low Resource Semi Supervised Abdominal Organ Segmentation
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Author : Jun Ma
language : en
Publisher: Springer Nature
Release Date : 2023-01-20
Fast And Low Resource Semi Supervised Abdominal Organ Segmentation written by Jun Ma and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-20 with Computers categories.
This book constitutes the proceedings of the MICCAI 2022 Challenge, FLARE 2022, held in Conjunction with MICCAI 2022, in Singapore, on September 22, 2022. The 28 full papers presented in this book were carefully reviewed and selected from 48 submissions. The papers present research and results for abdominal organ segmentation which has many important clinical applications, such as organ quantification, surgical planning, and disease diagnosis.
Landmarking And Segmentation Of 3d Ct Images
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Author : Shantanu Banik
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2009-04-08
Landmarking And Segmentation Of 3d Ct Images written by Shantanu Banik and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-08 with Technology & Engineering categories.
Segmentation and landmarking of computed tomographic (CT) images of pediatric patients are important and useful in computer-aided diagnosis (CAD), treatment planning, and objective analysis of normal as well as pathological regions. Identification and segmentation of organs and tissues in the presence of tumors are difficult. Automatic segmentation of the primary tumor mass in neuroblastoma could facilitate reproducible and objective analysis of the tumor's tissue composition, shape, and size. However, due to the heterogeneous tissue composition of the neuroblastic tumor, ranging from low-attenuation necrosis to high-attenuation calcification, segmentation of the tumor mass is a challenging problem. In this context, methods are described in this book for identification and segmentation of several abdominal and thoracic landmarks to assist in the segmentation of neuroblastic tumors in pediatric CT images. Methods to identify and segment automatically the peripheral artifacts and tissues, the rib structure, the vertebral column, the spinal canal, the diaphragm, and the pelvic surface are described. Techniques are also presented to evaluate quantitatively the results of segmentation of the vertebral column, the spinal canal, the diaphragm, and the pelvic girdle by comparing with the results of independent manual segmentation performed by a radiologist. The use of the landmarks and removal of several tissues and organs are shown to assist in limiting the scope of the tumor segmentation process to the abdomen, to lead to the reduction of the false-positive error, and to improve the result of segmentation of neuroblastic tumors. Table of Contents: Introduction to Medical Image Analysis / Image Segmentation / Experimental Design and Database / Ribs, Vertebral Column, and Spinal Canal / Delineation of the Diaphragm / Delineation of the Pelvic Girdle / Application of Landmarking / Concluding Remarks
Automated Imaging Based Abdominal Organ Segmentation And Quality Control In 20 000 Participants Of The Uk Biobank And German National Cohort Studies
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Author : Turkay Kart
language : en
Publisher:
Release Date : 2022
Automated Imaging Based Abdominal Organ Segmentation And Quality Control In 20 000 Participants Of The Uk Biobank And German National Cohort Studies written by Turkay Kart and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Abstract: Large epidemiological studies such as the UK Biobank (UKBB) or German National Cohort (NAKO) provide unprecedented health-related data of the general population aiming to better understand determinants of health and disease. As part of these studies, Magnetic Resonance Imaging (MRI) is performed in a subset of participants allowing for phenotypical and functional characterization of different organ systems. Due to the large amount of imaging data, automated image analysis is required, which can be performed using deep learning methods, e. g. for automated organ segmentation. In this paper we describe a computational pipeline for automated segmentation of abdominal organs on MRI data from 20,000 participants of UKBB and NAKO and provide results of the quality control process. We found that approx. 90% of data sets showed no relevant segmentation errors while relevant errors occurred in a varying proportion of data sets depending on the organ of interest. Image-derived features based on automated organ segmentations showed relevant deviations of varying degree in the presence of segmentation errors. These results show that large-scale, deep learning-based abdominal organ segmentation on MRI data is feasible with overall high accuracy, but visual quality control remains an important step ensuring the validity of down-stream analyses in large epidemiological imaging studies