10 citations · 18 across the 7 of their papers we have counts for
7 papers
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…
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…
AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detection
Mehdi Astaraki, Francesca De Benetti, Yousef Yeganeh +5
Robust and accurate detection and segmentation of heterogenous tumors appearing in different anatomical organs with supervised methods require large-scale labeled datasets covering…
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…
Development and evaluation of a 3D annotation software for interactive COVID-19 lesion segmentation in chest CT
Simone Bendazzoli, Irene Brusini, Mehdi Astaraki +7
Segmentation of COVID-19 lesions from chest CT scans is of great importance for better diagnosing the disease and investigating its extent. However, manual segmentation can be very…