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

Background Numerous features are commonly generated in radiomics applications as
applied to medical imaging, and identification of robust radiomics features (RFs) can be an …

Background: Although handcrafted radiomics features (RF) are commonly extracted via
radiomics software, employing deep features (DF) extracted from deep learning (DL) …

Prior to the deep learning era, shape was commonly used to describe the objects.
Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly …

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 …

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

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 …

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

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