Medical image segmentation is a fundamental and challenging problem for analyzing
medical images. Among different existing medical image segmentation methods, graph …

World-wide incidence rate of prostate cancer has progressively increased with time
especially with the increased proportion of elderly population. Early detection of prostate …

Convolutional neural networks (CNNs) have revolutionized medical image analysis over the
past few years. The U-Net architecture is one of the most well-known CNN architectures for …

Purpose Multi-organ segmentation from CT images is an essential step for computer-aided
diagnosis and surgery planning. However, manual delineation of the organs by radiologists …

All medical image segmentation algorithms need to be validated and compared, yet no
evaluation framework is widely accepted within the imaging community. None of the …

A robust automated segmentation of abdominal organs can be crucial for computer aided
diagnosis and laparoscopic surgery assistance. Many existing methods are specialized to …

This paper addresses the automated segmentation of multiple organs in upper abdominal
computed tomography (CT) data. The aim of our study is to develop methods to effectively …

An automated segmentation method is presented for multi-organ segmentation in abdominal
CT images. Dictionary learning and sparse coding techniques are used in the proposed …

Medical image segmentation is a key topic in image processing and computer vision.
Existing literature mainly focuses on single-organ segmentation. However, since maximizing …

Localization of anatomical regions of interest (ROIs) is a preprocessing step in many
medical image analysis tasks. While trivial for humans, it is complex for automatic methods …