Advances in biological and medical technologies have been providing us explosive
volumes of biological and physiological data, such as medical images …

The medical image analysis field has traditionally been focused on the development of
organ-, and disease-specific methods. Recently, the interest in the development of more …

High-resolution volume reconstruction from multiple motion-corrupted stacks of 2D slices
plays an increasing role for fetal brain Magnetic Resonance Imaging (MRI) studies …

Robust and fast detection of anatomical structures is a prerequisite for both diagnostic and
interventional medical image analysis. Current solutions for anatomy detection are typically …

Deep learning (DL) has become an important machine learning approach that has been
widely successful in many applications. Currently, DL is one of the best methods of …

Automatic detection of anatomical landmarks is an important step for a wide range of
applications in medical image analysis. Manual annotation of landmarks is a tedious task …

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 …

Organ localization is an essential preprocessing step for many medical image analysis
tasks, such as image registration, organ segmentation, and lesion detection. In this paper …

We propose a deep learning solution to the problem of object detection in 3D CT images, ie
the localization and classification of multiple structures. Supervised learning methods …

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