Radiomics is an emerging translational field of research aiming to extract mineable high-
dimensional data from clinical images. The radiomic process can be divided into distinct …

Image processing techniques being crucial towards analyzing and resolving issues in
medical imaging since last two decades. Medical imaging is a process or technique to find …

Medical image segmentation can provide a reliable basis for further clinical analysis and
disease diagnosis. With the development of convolutional neural networks (CNNs), medical …

Automatic gastric tumor segmentation and lymph node (LN) classification not only can assist
radiologists in reading images, but also provide image-guided clinical diagnosis and …

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 …

The computer-based process of identifying the boundaries of lung from surrounding thoracic
tissue on computed tomographic (CT) images, which is called segmentation, is a vital first …

Annotating multiple organs in medical images is both costly and time-consuming; therefore,
existing multi-organ datasets with labels are often low in sample size and mostly partially …

Deep learning has demonstrated tremendous revolutionary changes in the computing
industry and its effects in radiology and imaging sciences have begun to dramatically …

Liver segmentation has always been the focus of researchers because it plays an important
role in medical diagnosis. However, under the condition of low contrast between a liver and …

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