This chapter focuses on modern deep learning techniques that are proposed for
automatically recognizing and segmenting multiple organ regions on three-dimensional …

The purpose of this study is to evaluate and compare the performance of modern deep
learning techniques for automatically recognizing and segmenting multiple organ regions on …

We have proposed an end-to-end learning approach that trained a deep convolutional
neural network (CNN) for automatic CT image segmentation, which accomplished a voxel …

Deep learning-based methods achieved impressive results for the segmentation of medical
images. With the development of 3D fully convolutional networks (FCNs), it has become …

Purpose We propose a single network trained by pixel‐to‐label deep learning to address
the general issue of automatic multiple organ segmentation in three‐dimensional (3D) …

Computer-aided medical image analysis plays a significant role in assisting medical
practitioners for expert clinical diagnosis and deciding the optimal treatment plan. At present …

We propose a novel approach for automatic segmentation of anatomical structures on 3D
CT images by voting from a fully convolutional network (FCN), which accomplishes an end …

Purpose: Accurate segmentation of treatment planning computed tomography (CT) images
is important for radiation therapy (RT) planning. However, low soft tissue contrast in CT …

Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …

Computed tomography images are scans that combine a series of X-rays with computer
processing techniques to display organs in the body. Recently, 3D CNN models have …