Recently, deep convolutional neural networks have achieved great success for medical
image segmentation. However, unlike segmentation of natural images, most medical images …

Accurate segmentation of multiple organs of the head, neck, chest, and abdomen from
medical images is an essential step in computer-aided diagnosis, surgical navigation, and …

Radiotherapy with precise segmentation of head and neck organs at risk (OARs) is one of
the important treatment methods for head and neck cancer. In routine clinical practice, OARs …

Objective This paper intends to propose a method of using TransResSEUnet2. 5D network
for accurate automatic segmentation of the Gross Target Volume (GTV) in Radiotherapy for …

MRI and CT images have been routinely used in clinical practice for treatment planning of
the head‐and‐neck (HAN) radiotherapy. Delineating organs‐at‐risk (OAR) is an essential …

Precise delineation of multiple organs or abnormal regions in the human body from medical
images plays an essential role in computer-aided diagnosis, surgical simulation, image …

Purpose The segmentation of organs at risk (OAR) is a required precondition for the cancer
treatment with image-guided radiation therapy. The automation of the segmentation task is …

Abstract Bayesian Neural Nets (BNN) are increasingly used for robust organ auto-
contouring. Uncertainty heatmaps extracted from BNNs have been shown to correspond to …

Deep learning models for organ contouring in radiotherapy are poised for clinical usage, but
currently, there exist few tools for automated quality assessment (QA) of the predicted …

Precise cervical cancer treatment highly relies on accurate segmentation of cervical tumors
from magnetic resonance (MR) images. However, this task is challenged by the …