Abstract The Segmentation Anything Model (SAM) has demonstrated effectiveness in
various segmentation tasks. However, its application to 3D medical data has posed …

Pelvic floor dysfunction is common in women after childbirth and precise segmentation of
magnetic resonance images (MRI) of the pelvic floor may facilitate diagnosis and treatment …

It has been shown that employing multiple atlas images improves segmentation accuracy in
atlas-based medical image segmentation. Each atlas image is registered to the target image …

Combination of multiple segmentations has recently been introduced as an effective method
to obtain segmentations that are more accurate than any of the individual input …

Nowadays U-net-like FCNs predominate various biomedical image segmentation
applications and attain promising performance, largely due to their elegant architectures, eg …

A system for the fully automatic segmentation of the liver and spleen is presented. In a multi-
atlas based segmentation framework, several existing segmentations are deformed in …

Image segmentation is an important task in many medical applications. Methods based on
convolutional neural networks attain state-of-the-art accuracy; however, they typically rely on …

Despite recent progress of deep learning-based medical image segmentation techniques,
fully automatic results often fail to meet clinically acceptable accuracy, especially when …

Abstract Medical Image Segmentation is the process of segmenting and detecting
boundaries of anatomical structures in various types of 2D and 3D-medical images. The …

Medical image segmentation, the task of partitioning an image into meaningful parts, is an
important step toward automating medical image analysis and is at the crux of a variety of …