In recent years, the segmentation of anatomical or pathological structures using deep
learning has experienced a widespread interest in medical image analysis. Remarkably …

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark
(LiTS), which was organized in conjunction with the IEEE International Symposium on …

Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular
carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor …

Objective: Accurate segmentation of liver tumors, which could help physicians make
appropriate treatment decisions and assess the effectiveness of surgical treatment, is crucial …

Objectives Machine learning (ML) for medical imaging is emerging for several organs and
image modalities. Our objectives were to provide clinicians with an overview of this field by …

Deep convolutional neural networks have been widely used for medical image
segmentation due to their superiority in feature learning. Although these networks are …

Abdominal anatomy segmentation is crucial for numerous applications from computer-
assisted diagnosis to image-guided surgery. In this context, we address fully-automated …

The diagnosis of liver cancer is one of the most attractive fields in clinical practice for its high
mortality. Accurate segmentation of liver and tumor has been publicly accepted to be an …

Purpose Liver tumor segmentation is a crucial prerequisite for computer‐aided diagnosis of
liver tumors. In the clinical diagnosis of liver tumors, radiologists usually examine multiphase …

Purpose The accurate segmentation of liver and liver tumors from CT images can assist
radiologists in decision‐making and treatment planning. The contours of liver and liver …