Deep learning‐based automated abdominal organ segmentation in the UK Biobank and German National Cohort Magnetic Resonance Imaging Studies
Purpose The aims of this study were to train and evaluate deep learning models for
automated segmentation of abdominal organs in whole-body magnetic resonance (MR) …
automated segmentation of abdominal organs in whole-body magnetic resonance (MR) …
[HTML][HTML] Automated imaging-based abdominal organ segmentation and quality control in 20,000 participants of the UK Biobank and German National Cohort Studies
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 …
(NAKO) provide unprecedented health-related data of the general population aiming to …
[HTML][HTML] AbdomenNet: deep neural network for abdominal organ segmentation in epidemiologic imaging studies
Background Whole-body imaging has recently been added to large-scale epidemiological
studies providing novel opportunities for investigating abdominal organs. However, the …
studies providing novel opportunities for investigating abdominal organs. However, the …
Fully automated multiorgan segmentation in abdominal magnetic resonance imaging with deep neural networks
Purpose Segmentation of multiple organs‐at‐risk (OARs) is essential for magnetic
resonance (MR)‐only radiation therapy treatment planning and MR‐guided adaptive …
resonance (MR)‐only radiation therapy treatment planning and MR‐guided adaptive …
Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation
Despite the considerable progress in automatic abdominal multi-organ segmentation from
CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is …
CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is …
Fully convolutional neural networks improve abdominal organ segmentation
Abdominal image segmentation is a challenging, yet important clinical problem. Variations
in body size, position, and relative organ positions greatly complicate the segmentation …
in body size, position, and relative organ positions greatly complicate the segmentation …
Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CF s), convolutional neural networks …
Purpose As part of a program to implement automatic lesion detection methods for whole
body magnetic resonance imaging (MRI) in oncology, we have developed, evaluated, and …
body magnetic resonance imaging (MRI) in oncology, we have developed, evaluated, and …
Automatic multi-organ segmentation on abdominal CT with dense V-networks
Automatic segmentation of abdominal anatomy on computed tomography (CT) images can
support diagnosis, treatment planning, and treatment delivery workflows. Segmentation …
support diagnosis, treatment planning, and treatment delivery workflows. Segmentation …
Liver, kidney and spleen segmentation from CT scans and MRI with deep learning: A survey
Deep Learning approaches for automatic segmentation of organs from CT scans and MRI
are providing promising results, leading towards a revolution in the radiologists' workflow …
are providing promising results, leading towards a revolution in the radiologists' workflow …
[HTML][HTML] Deep learning with limited data: organ segmentation performance by U-Net
(1) Background: The effectiveness of deep learning artificial intelligence depends on data
availability, often requiring large volumes of data to effectively train an algorithm. However …
availability, often requiring large volumes of data to effectively train an algorithm. However …