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 …
Advances in auto-segmentation
Manual image segmentation is a time-consuming task routinely performed in radiotherapy to
identify each patient's targets and anatomical structures. The efficacy and safety of the …
identify each patient's targets and anatomical structures. The efficacy and safety of the …
Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy
In radiotherapy for cancer patients, an indispensable process is to delineate organs-at-risk
(OARs) and tumors. However, it is the most time-consuming step as manual delineation is …
(OARs) and tumors. However, it is the most time-consuming step as manual delineation is …
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 …
3D whole brain segmentation using spatially localized atlas network tiles
Detailed whole brain segmentation is an essential quantitative technique in medical image
analysis, which provides a non-invasive way of measuring brain regions from a clinical …
analysis, which provides a non-invasive way of measuring brain regions from a clinical …
Multi-atlas segmentation of biomedical images: a survey
Abstract Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering
work of Rohlfing, et al.(2004), Klein, et al.(2005), and Heckemann, et al.(2006), is becoming …
work of Rohlfing, et al.(2004), Klein, et al.(2005), and Heckemann, et al.(2006), is becoming …
Multi-scale deep reinforcement learning for real-time 3D-landmark detection in CT scans
Robust and fast detection of anatomical structures is a prerequisite for both diagnostic and
interventional medical image analysis. Current solutions for anatomy detection are typically …
interventional medical image analysis. Current solutions for anatomy detection are typically …
Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI
X Zhuang, J Shen - Medical image analysis, 2016 - Elsevier
A whole heart segmentation (WHS) method is presented for cardiac MRI. This segmentation
method employs multi-modality atlases from MRI and CT and adopts a new label fusion …
method employs multi-modality atlases from MRI and CT and adopts a new label fusion …
Medical image registration: a review
This paper presents a review of automated image registration methodologies that have been
used in the medical field. The aim of this paper is to be an introduction to the field, provide …
used in the medical field. The aim of this paper is to be an introduction to the field, provide …
Automated segmentation of the hypothalamus and associated subunits in brain MRI
Despite the crucial role of the hypothalamus in the regulation of the human body,
neuroimaging studies of this structure and its nuclei are scarce. Such scarcity partially stems …
neuroimaging studies of this structure and its nuclei are scarce. Such scarcity partially stems …