10 citations · 15 across the 6 of their papers we have counts for
7 papers
Anatomy-Aware Lymphoma Lesion Detection in Whole-Body PET/CT
Simone Bendazzoli, Antonios Tzortzakakis, Andreas Abrahamsson +4
Early cancer detection is crucial for improving patient outcomes, and 18F FDG PET/CT imaging plays a vital role by combining metabolic and anatomical information. Accurate lesion d…
MAIA: A Collaborative Medical AI Platform for Integrated Healthcare Innovation
Simone Bendazzoli, Sanna Persson, Mehdi Astaraki +3
The integration of Artificial Intelligence (AI) into clinical workflows requires robust collaborative platforms that are able to bridge the gap between technical innovation and pra…
Lesion Segmentation in Whole-Body Multi-Tracer PET-CT Images; a Contribution to AutoPET 2024 Challenge
Mehdi Astaraki, Simone Bendazzoli
The automatic segmentation of pathological regions within whole-body PET-CT volumes has the potential to streamline various clinical applications such as diagno-sis, prognosis, and…
Unsupervised Domain Adaptation for Pediatric Brain Tumor Segmentation
Jingru Fu, Simone Bendazzoli, Örjan Smedby +1
Significant advances have been made toward building accurate automatic segmentation models for adult gliomas. However, the performance of these models often degrades when applied t…
SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
Xiangde Luo, Jia Fu, Yunxin Zhong +39
Radiation therapy is a primary and effective NasoPharyngeal Carcinoma (NPC) treatment strategy. The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is c…
Fully Automatic Segmentation of Gross Target Volume and Organs-at-Risk for Radiotherapy Planning of Nasopharyngeal Carcinoma
Mehdi Astaraki, Simone Bendazzoli, Iuliana Toma-Dasu
Target segmentation in CT images of Head&Neck (H&N) region is challenging due to low contrast between adjacent soft tissue. The SegRap 2023 challenge has been focused on benchmarki…
PriorNet: lesion segmentation in PET-CT including prior tumor appearance information
Simone Bendazzoli, Mehdi Astaraki
Tumor segmentation in PET-CT images is challenging due to the dual nature of the acquired information: low metabolic information in CT and low spatial resolution in PET. U-Net arch…