Abstract
Radiological follow-up of oncological patients requires the analysis and comparison of multiple unregistered scans acquired every few months. This process is currently partial, time-consuming and subject to variability. We present a new, generic, graph-based method for tracking individual lesion changes and detecting patterns in the evolution of lesions over time. The tasks are formalized as graph-theoretic problems in which lesions are vertices and edges are lesion pairings computed by overlap-based lesion matching. We define seven individual lesion change classes and five lesion change patterns that fully summarize the evolution of lesions over time. They are directly computed from the graph properties and its connected components with graph-based methods. Experimental results on lung (83 CTs from 19 patients) and liver (77 CECTs from 18 patients) datasets with more than two scans per patient yielded an individual lesion change class accuracy of 98% and 85%, and identification of patterns of lesion change with an accuracy of 96% and 76%, respectively. Highlighting unusual lesion labels and lesion change patterns in the graph helps radiologists identify overlooked or faintly visible lesions. Automatic lesion change classification and pattern detection in longitudinal studies may improve the accuracy and efficiency of radiological interpretation and disease status evaluation.
Original language | English |
---|---|
Title of host publication | Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings |
Editors | Hayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 106-115 |
Number of pages | 10 |
ISBN (Print) | 9783031439032 |
DOIs | |
State | Published - 2023 |
Event | 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 - Vancouver, Canada Duration: 8 Oct 2023 → 12 Oct 2023 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
---|---|
Volume | 14224 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 |
---|---|
Country/Territory | Canada |
City | Vancouver |
Period | 8/10/23 → 12/10/23 |
Bibliographical note
Publisher Copyright:© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Keywords
- lesion change analysis
- lesion matching
- longitudinal follow-up