Head and Neck Tumor Segmentation and Outcome Prediction
Third Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings
Page
%P
39 Result(s)
within
Adrien Depeursinge
English
-
Article
Open AccessComparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [82Rb] PET for MACE prediction
Assessing the individual risk of Major Adverse Cardiac Events (MACE) is of major importance as cardiovascular diseases remain the leading cause of death worldwide. Quantitative Myocardial Perfusion Imaging (MP...
-
Article
Open AccessA global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences
Since its emergence in the 1960s, Artificial Intelligence (AI) has grown to conquer many technology products and their fields of application. Machine learning, as a major part of the current AI solutions, can ...
-
Article
Open AccessQuantImage v2: a comprehensive and integrated physician-centered cloud platform for radiomics and machine learning research
Radiomics, the field of image-based computational medical biomarker research, has experienced rapid growth over the past decade due to its potential to revolutionize the development of personalized decision su...
-
Book and Conference Proceedings
-
Chapter and Conference Paper
Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT
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 of the 25th International Conference on M...
-
Article
Open AccessReproducibility of lung cancer radiomics features extracted from data-driven respiratory gating and free-breathing flow imaging in [18F]-FDG PET/CT
Quality and reproducibility of radiomics studies are essential requirements for the standardisation of radiomics models. As recent data-driven respiratory gating (DDG) [18F]-FDG has shown superior diagnostic perf...
-
Article
Open AccessAssessing radiomics feature stability with simulated CT acquisitions
Medical imaging quantitative features had once disputable usefulness in clinical studies. Nowadays, advancements in analysis techniques, for instance through machine learning, have enabled quantitative feature...
-
Chapter and Conference Paper
Comparison of MR Preprocessing Strategies and Sequences for Radiomics-Based MGMT Prediction
Hypermethylation of the O6-methylguanine-DNA-methyltransferase (MGMT) promoter in glioblastoma (GBM) is a predictive biomarker associated with improved treatment outcome. In clinical practice, MGMT methylation...
-
Book and Conference Proceedings
Head and Neck Tumor Segmentation and Outcome Prediction
Second Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings
-
Chapter and Conference Paper
Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images
This paper presents an overview of the second edition of the HEad and neCK TumOR (HECKTOR) challenge, organized as a satellite event of the 24th International Conference on Medical Image Computing and Computer...
-
Book and Conference Proceedings
Head and Neck Tumor Segmentation
First Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings
-
Chapter and Conference Paper
Multi-task Deep Segmentation and Radiomics for Automatic Prognosis in Head and Neck Cancer
We propose a novel method for the prediction of patient prognosis with Head and Neck cancer (H&N) from FDG-PET/CT images. In particular, we aim at automatically predicting Disease-Free Survival (DFS) for patie...
-
Chapter and Conference Paper
Fully Automatic Head and Neck Cancer Prognosis Prediction in PET/CT
Several recent PET/CT radiomics studies have shown promising results for the prediction of patient outcomes in Head and Neck (H&N) cancer. These studies, however, are most often conducted on relatively small c...
-
Chapter and Conference Paper
Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT
This paper presents an overview of the first HEad and neCK TumOR (HECKTOR) challenge, organized as a satellite event of the 23rd International Conference on Medical Image Computing and Computer Assisted Interv...
-
Article
Open AccessThe importance of feature aggregation in radiomics: a head and neck cancer study
In standard radiomics studies the features extracted from clinical images are mostly quantified with simple statistics such as the average or variance per Region of Interest (ROI). Such approaches may smooth o...
-
Article
Open AccessIntegrating radiomics into holomics for personalised oncology: from algorithms to bedside
Radiomics, artificial intelligence, and deep learning figure amongst recent buzzwords in current medical imaging research and technological development. Analysis of medical big data in assessment and follow-up...
-
Chapter and Conference Paper
Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging
Image scale carries crucial information in medical imaging, e.g. the size and spatial frequency of local structures, lesions, tumors and cell nuclei. With feature transfer being a common practice, scale-invari...
-
Article
Open AccessRevealing Tumor Habitats from Texture Heterogeneity Analysis for Classification of Lung Cancer Malignancy and Aggressiveness
We propose an approach for characterizing structural heterogeneity of lung cancer nodules using Computed Tomography Texture Analysis (CTTA). Measures of heterogeneity were used to test the hypothesis that hete...
-
Chapter and Conference Paper
Rotation Invariance and Directional Sensitivity: Spherical Harmonics versus Radiomics Features
We define and investigate the Local Rotation Invariance (LRI) and Directional Sensitivity (DS) of radiomics features. Most of the classical features cannot combine the two properties, which are antagonist in s...
-
Chapter and Conference Paper
Holographic Visualisation and Interaction of Fused CT, PET and MRI Volumetric Medical Imaging Data Using Dedicated Remote GPGPU Ray Casting
Medical experts commonly use imaging including Computed Tomography (CT), Positron-Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) for diagnosis or to plan a surgery. These scans give a highly de...
