Deep learning technologies have dramatically reshaped the field of medical image
registration over the past decade. The initial developments, such as regression-based and U …

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 …

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 …

Purpose Develop a universal lesion recognition algorithm for PET/CT and PET/MRI, validate
it, and explore factors affecting performance. Procedures The 2022 AutoPet Challenge's …

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

Background: Accurate tumor detection and quantification are important for optimized therapy
planning and evaluation. Total tumor burden is also an appealing biomarker for clinical …

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

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

Automatic multi-organ segmentation in medical image anal-ysis is a crucial task with various
applications in computer-aided diag-nosis and treatment. Convolutional neural networks …