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 …
MRI segmentation of the human brain: challenges, methods, and applications
I Despotović, B Goossens… - … mathematical methods in …, 2015 - Wiley Online Library
Image segmentation is one of the most important tasks in medical image analysis and is
often the first and the most critical step in many clinical applications. In brain MRI analysis …
often the first and the most critical step in many clinical applications. In brain MRI analysis …
Automatic multiorgan segmentation in thorax CT images using U‐net‐GAN
Purpose Accurate and timely organs‐at‐risk (OARs) segmentation is key to efficient and
high‐quality radiation therapy planning. The purpose of this work is to develop a deep …
high‐quality radiation therapy planning. The purpose of this work is to develop a deep …
ST-unet: Swin transformer boosted U-net with cross-layer feature enhancement for medical image segmentation
J Zhang, Q Qin, Q Ye, T Ruan - Computers in Biology and Medicine, 2023 - Elsevier
Medical image segmentation is an essential task in clinical diagnosis and case analysis.
Most of the existing methods are based on U-shaped convolutional neural networks (CNNs) …
Most of the existing methods are based on U-shaped convolutional neural networks (CNNs) …
Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation
Accurate and automatic organ segmentation from 3D radiological scans is an important yet
challenging problem for medical image analysis. Specifically, as a small, soft, and flexible …
challenging problem for medical image analysis. Specifically, as a small, soft, and flexible …
A survey of Hough Transform
P Mukhopadhyay, BB Chaudhuri - Pattern Recognition, 2015 - Elsevier
In 1962 Hough earned the patent for a method [1], popularly called Hough Transform (HT)
that efficiently identifies lines in images. It is an important tool even after the golden jubilee …
that efficiently identifies lines in images. It is an important tool even after the golden jubilee …
Synthetic MRI-aided multi-organ segmentation on male pelvic CT using cycle consistent deep attention network
Background and purpose Manual contouring is labor intensive, and subject to variations in
operator knowledge, experience and technique. This work aims to develop an automated …
operator knowledge, experience and technique. This work aims to develop an automated …
Statistical shape models for 3D medical image segmentation: a review
T Heimann, HP Meinzer - Medical image analysis, 2009 - Elsevier
Statistical shape models (SSMs) have by now been firmly established as a robust tool for
segmentation of medical images. While 2D models have been in use since the early 1990s …
segmentation of medical images. While 2D models have been in use since the early 1990s …
Review of deep learning based automatic segmentation for lung cancer radiotherapy
X Liu, KW Li, R Yang, LS Geng - Frontiers in oncology, 2021 - frontiersin.org
Lung cancer is the leading cause of cancer-related mortality for males and females.
Radiation therapy (RT) is one of the primary treatment modalities for lung cancer. While …
Radiation therapy (RT) is one of the primary treatment modalities for lung cancer. While …
Male pelvic multi-organ segmentation using token-based transformer Vnet
Objective. This work aims to develop an automated segmentation method for the prostate
and its surrounding organs-at-risk in pelvic computed tomography to facilitate prostate …
and its surrounding organs-at-risk in pelvic computed tomography to facilitate prostate …