Manual segmentation of target structures and organs at risk is a crucial step in the
radiotherapy workflow. It has the disadvantages that it can require several hours of clinician …

In recent years, the segmentation of anatomical or pathological structures using deep
learning has experienced a widespread interest in medical image analysis. Remarkably …

Simple Summary With a five-year survival rate of only 3% for the majority of patients,
pancreatic cancer is a global healthcare challenge. Radiomics and deep learning, two novel …

Online adaptive radiation therapy aims at adapting a patient's treatment plan to their current
anatomy to account for inter-fraction variations before daily treatment delivery. As this …

Molecular imaging is one of the pillars of precision surgery. Its applications range from early
diagnostics to therapy planning, execution, and the accurate assessment of outcomes. In …

Objectives This study aims to evaluate an automatic segmentation algorithm for pharyngeal
airway in cone‐beam computed tomography (CBCT) images using a deep learning artificial …

Objectives In radiomics, high-throughput algorithms extract objective quantitative features
from medical images. In this study, we evaluated CT-based radiomics features, clinical …

Understanding model predictions is critical in healthcare, to facilitate rapid verification of
model correctness and to guard against use of models that exploit confounding variables …

Computed tomography images are scans that combine a series of X-rays with computer
processing techniques to display organs in the body. Recently, 3D CNN models have …

Brain tumour segmentation is a requirement of many quantitative MRI analyses involving
glioma. This paper argues that 2D slice-wise approaches to brain tumour segmentation may …