Deep learning algorithms, in particular convolutional networks, have rapidly become a
methodology of choice for analyzing medical images. This paper reviews the major deep …

Recent advancements and major breakthroughs in machine learning (ML) technologies in
the past decade have made it possible to collect, analyze, and interpret an unprecedented …

Purpose Delineation of the clinical target volume (CTV) and organs at risk (OAR s) is very
important for radiotherapy but is time‐consuming and prone to inter‐observer variation …

In this paper, we introduce a simple, yet powerful pipeline for medical image segmentation
that combines Fully Convolutional Networks (FCNs) with Fully Convolutional Residual …

Background Radiotherapy is one of the main treatment methods for nasopharyngeal
carcinoma (NPC). It requires exact delineation of the nasopharynx gross tumor volume …

Abstract Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most common
inherited disorder of the kidneys. It is characterized by enlargement of the kidneys caused by …

The explosion of digital healthcare data has led to a surge of data-driven medical research
based on machine learning. In recent years, as a powerful technique for big data, deep …

Automated medical assistance system is in high demand with the advances in research in
the machine learning area. In many such applications, availability of labeled medical dataset …

Background The most tedious and time-consuming task in medical additive manufacturing
(AM) is image segmentation. The aim of the present study was to develop and train a …

Renal segmentation is one of the most fundamental and challenging task in computer aided
diagnosis systems. In order to overcome the shortcomings of automatic kidney segmentation …