Graph-Theoretic Automatic Lesion Tracking and Detection of Patterns of Lesion Changes in Longitudinal CT Studies

Beniamin Di Veroli, Richard Lederman, Jacob Sosna, Leo Joskowicz*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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 languageAmerican English
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings
EditorsHayit Greenspan, Hayit Greenspan, Anant Madabhushi, Parvin Mousavi, Septimiu Salcudean, James Duncan, Tanveer Syeda-Mahmood, Russell Taylor
PublisherSpringer Science and Business Media Deutschland GmbH
Pages106-115
Number of pages10
ISBN (Print)9783031439032
DOIs
StatePublished - 2023
Event26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 - Vancouver, Canada
Duration: 8 Oct 202312 Oct 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14224 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023
Country/TerritoryCanada
CityVancouver
Period8/10/2312/10/23

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

Fingerprint

Dive into the research topics of 'Graph-Theoretic Automatic Lesion Tracking and Detection of Patterns of Lesion Changes in Longitudinal CT Studies'. Together they form a unique fingerprint.

Cite this