Multimodal medical imaging plays a pivotal role in clinical diagnosis and research, as it
combines information from various imaging modalities to provide a more comprehensive …

Brain tumors rank as the 1oth leading cause of mortality worldwide, accounting for 85% to
95% of all primary nervous system malignancies. The prevalence of this life-threatening …

This paper presents an overview of the third edition of the HEad and neCK TumOR
segmentation and outcome prediction (HECKTOR) challenge, organized as a satellite event …

By focusing on metabolic and morphological tissue properties respectively,
FluoroDeoxyGlucose (FDG)-Positron Emission Tomography (PET) and Computed …

Background Radiomics can provide in-depth characterization of cancers for treatment
outcome prediction. Conventional radiomics rely on extraction of image features within a pre …

Autosegmentation of gross tumor volumes holds promise to decrease clinical demand and
to provide consistency across clinicians and institutions for radiation treatment planning …

Abstract Background and Objective Generalizable and trustworthy deep learning models for
PET/CT image segmentation necessitates large diverse multi-institutional datasets …

Featured Application Enhancing Clinical Diagnosis through the Integration of Deep
Learning Techniques in Medical Image Recognition. This comprehensive review highlights …

Objective. Tumor segmentation is a fundamental step for radiotherapy treatment planning.
To define an accurate segmentation of the primary tumor (GTVp) of oropharyngeal cancer …

Purpose Accurately segmenting head and neck cancer (HNC) tumors in medical images is
crucial for effective treatment planning. However, current methods for HNC segmentation are …