A review of deep learning based methods for medical image multi-organ segmentation
Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …
performance in many medical image segmentation tasks. Many deep learning-based …
Convolutional neural network in medical image analysis: a review
SS Kshatri, D Singh - Archives of Computational Methods in Engineering, 2023 - Springer
Medical image analysis helps in resolving clinical issues by examining clinically generated
images. In today's world of deep learning (DL) along with advances in computer vision, the …
images. In today's world of deep learning (DL) along with advances in computer vision, the …
Cotr: Efficiently bridging cnn and transformer for 3d medical image segmentation
Convolutional neural networks (CNNs) have been the de facto standard for nowadays 3D
medical image segmentation. The convolutional operations used in these networks …
medical image segmentation. The convolutional operations used in these networks …
Abdomenct-1k: Is abdominal organ segmentation a solved problem?
With the unprecedented developments in deep learning, automatic segmentation of main
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …
Dodnet: Learning to segment multi-organ and tumors from multiple partially labeled datasets
Due to the intensive cost of labor and expertise in annotating 3D medical images at a voxel
level, most benchmark datasets are equipped with the annotations of only one type of …
level, most benchmark datasets are equipped with the annotations of only one type of …
3D multi-attention guided multi-task learning network for automatic gastric tumor segmentation and lymph node classification
Automatic gastric tumor segmentation and lymph node (LN) classification not only can assist
radiologists in reading images, but also provide image-guided clinical diagnosis and …
radiologists in reading images, but also provide image-guided clinical diagnosis and …
Multi-modal co-learning for liver lesion segmentation on PET-CT images
Liver lesion segmentation is an essential process to assist doctors in hepatocellular
carcinoma diagnosis and treatment planning. Multi-modal positron emission tomography …
carcinoma diagnosis and treatment planning. Multi-modal positron emission tomography …
[HTML][HTML] A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patients
Background The assessment of the severity of coronavirus disease 2019 (COVID-19) by
clinical presentation has not met the urgent clinical need so far. We aimed to establish a …
clinical presentation has not met the urgent clinical need so far. We aimed to establish a …
Multi-organ segmentation network for abdominal CT images based on spatial attention and deformable convolution
N Shen, Z Wang, J Li, H Gao, W Lu, P Hu… - Expert Systems with …, 2023 - Elsevier
The accurate segmentation of multi-organ based on computed tomography (CT) images is
important for the diagnosis of abdominal diseases, such as cancer staging, and for surgical …
important for the diagnosis of abdominal diseases, such as cancer staging, and for surgical …
A statistical deformation model-based data augmentation method for volumetric medical image segmentation
The accurate delineation of organs-at-risk (OARs) is a crucial step in treatment planning
during radiotherapy, as it minimizes the potential adverse effects of radiation on surrounding …
during radiotherapy, as it minimizes the potential adverse effects of radiation on surrounding …