In many medical image analysis applications, only a limited amount of training data is
available due to the costs of image acquisition and the large manual annotation effort …

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 …

In this study, we propose a fast and accurate method to automatically localize anatomical
landmarks in medical images. We employ a global-to-local localization approach using fully …

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 …

We propose a pipeline of two fully convolutional networks for automatic multi-label whole
heart segmentation from CT and MRI volumes. At first, a convolutional neural network (CNN) …

Cephalometric analysis is a fundamental examination which is widely used in orthodontic
diagnosis and treatment planning. Its key step is to detect the anatomical landmarks in …

Detecting anatomical landmarks in medical images plays an essential role in understanding
the anatomy and planning automated processing. In recent years, a variety of deep neural …

In approaches for automatic localization of multiple anatomical landmarks, disambiguation
of locally similar structures as obtained by locally accurate candidate generation is often …

Cephalometric analysis relies on accurate detection of craniomaxillofacial (CMF) landmarks
from cone-beam computed tomography (CBCT) images. However, due to the complexity of …

Well-annotated medical images are costly and sometimes even impossible to acquire,
hindering landmark detection accuracy to some extent. Semi-supervised learning alleviates …