Interactive segmentation is a crucial research area in medical image analysis aiming to
boost the efficiency of costly annotations by incorporating human feedback. This feedback …

Multi-modal medical image segmentation is a crucial task in oncology that enables the
precise localization and quantification of tumors. The aim of this work is to present a meta …

Purpose Total metabolic tumor volume (TMTV) segmentation has significant value enabling
quantitative imaging biomarkers for lymphoma management. In this work, we tackle the …

Deep learning has revolutionized the accurate segmentation of diseases in medical
imaging. However, achieving such results requires training with numerous manual voxel …

Manual segmentation poses a time-consuming challenge for disease quantification, therapy
evaluation, treatment planning, and outcome prediction. Convolutional neural networks …

Early cancer detection, guided by whole-body imaging, is important for the overall survival
and well-being of the patients. While various computer-assisted systems have been …

Accurately segmenting abdominal organs and tumors within computed tomography (CT)
scans holds paramount significance for facilitating computer-aided diagnosis and devising …

Automated segmentation of cancerous lesions in PET/CT images is a vital initial task for
quantitative analysis. However, it is often challenging to train deep learning-based …

Unsupervised domain adaptation (UDA) is essential for medical image segmentation,
especially in cross-modality data scenarios. UDA aims to transfer knowledge from a labeled …

Deep-learning based models offer powerful tools for the automatic segmentation of
abdominal organs and tumors in CT scans, yet they face challenges such as limited datasets …