Segmentation models predominantly optimize pixel-overlap-based loss, an objective that is
actually inadequate for many segmentation tasks. In recent years, their limitations fueled a …

In digital pathology, segmenting densely distributed objects like glands and nuclei is crucial
for downstream analysis. Since detailed pixel-wise annotations are very time-consuming, we …

The morphological features of glands provide a reliable basis for pathologists to diagnose
colon cancer correctly. Currently, most methods are limited in their ability to address blurred …

Microscopic evaluation of glands in the colon is of utmost importance in the diagnosis of
inflammatory bowel disease and cancer. When properly trained, deep learning pipelines …

Existing deep neural networks for histopathology image synthesis cannot generate image
styles that align with different organs, and cannot produce accurate boundaries of clustered …

Accurate myocardial segmentation is crucial for the diagnosis of various heart diseases.
However, segmentation results often suffer from topology structural errors, such as broken …

Automatic vessel segmentation is a critical area of research in medical image analysis, as it
can greatly assist doctors in accurately and efficiently diagnosing vascular diseases …

While conventional computer vision emphasizes pixel-level and feature-based objectives,
medical image analysis of intricate biological structures necessitates explicit representation …

In clinical research, the segmentation of irregularly shaped nuclei, particularly in
mesenchymal areas like fibroblasts, is crucial yet often neglected. These irregular nuclei are …

In computational pathology, segmenting densely distributed objects like glands and nuclei is
crucial for downstream analysis. To alleviate the burden of obtaining pixel-wise annotations …