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Abstract #3801

Test-Retest analysis of atTRACTive with a dissimilarity uncertainty sampling scheme on the Corpus Callosum of mice

Robin Peretzke1,2, Jonas Bohn1,3,4, Yannick Kirchhoff1,5,6, Saikat Roy1,6, Julian Schroers7,8, Felix Tobias Kurz7, Pavlina Lenga9, Daniela Becker9,10, Geva Brandt11, Dusan Hirjak12, Klaus Maier-Hein1,13,14,15, and Peter Neher1,13,15
1German Cancer Research Center (DKFZ), Division of Medical Image Computing, Heidelberg, Germany, 2Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany, 3NCT Heidelberg, National Center for Tumor Diseases (NCT), Heidelberg, Germany, 4Faculty of Bioscience, Heidelberg University, Heidelberg, Germany, 5HIDSS4Health - Helmholtz Information and Data Science School for Health,, Karlsruhe/Heidelberg, Germany, 6Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany, 7German Cancer Research Center (DKFZ), Division of Radiology, Heidelberg, Germany, 8Neurology Clinic and National Center for Tumor Diseases, Heidelberg University Hospital, Heidelberg, Germany, 9Department of Neurosurgery, Heidelberg University Hospital, Heidelberg, Germany, 10IU, International University of Applied Sciences, Erfurt, Germany, 11Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany, 12Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Germany, 13National Center for Tumor Diseases (NCT), Heidelberg, Germany, 14Pattern Analysis and Learning Group,, Heidelberg University Hospital, Heidelberg, Germany, 15German Cancer Consortium (DKTK), partner site Heidelberg, Germany

Synopsis

Keywords: AI/ML Software, Machine Learning/Artificial Intelligence, Active Learning, Tractography, White Matter

Motivation: Accurate tractography-based segmentation of white matter tracts is crucial for tasks such as pre-surgical planning. Fully automated methods are limited to predefined tracts and struggle with anatomical deviations, e.g. caused by tumors.

Goal(s): Our goal is to enhance the manual segmentation process through a novel and intuitive approach.

Approach: We recently developed atTRACTive, a tool for semi-automatic fiber dissection relying on entropy-based active learning. In this work, we have improved atTRACTive and conducted an initial evaluation of its test-retest reliability in comparison to traditional ROI-based tract segmentation methods.

Results: atTRACTive has demonstrated superior test-retest reliability compared to traditional ROI-based segmentation approaches.

Impact: The method offers guidance to researchers in the intuitive and efficient segmentation of arbitrary white matter tracts. Instead of drawing challenging-to-reproduce ROIs, users can simply annotate meaningful streamlines, which are then used to train a classifier.

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Abstract

Keywords