Segmentation of glioma structures is vital for therapy planning. Although state of the art algorithms achieve impressive results when compared to ground-truth manual delineations, one could argue that the binary nature of these labels does not properly reflect the underlying biology, nor does it account for uncertainties in the predicted segmentations. Moreover, the tumor infiltration beyond the contrast-enhanced lesion – visually imperceptible on imaging – is often ignored despite its potential role in tumor recurrence. We propose an intensity-based probabilistic model for brain tissue mapping based on conventional MRI sequences. We evaluated its value in the binary segmentation of the tumor and its subregions, and in the visualisation of possible infiltration. The model achieves a median Dice of 0.82 in the detection of the whole tumor, but suffers from confusion between different subregions. Preliminary results for the tumor probability maps encourage further investigation of the model regarding infiltration detection.
De Sutter, S, Geens, W, Bossa, M, Vanbinst, AM, Duerinck, J & Vandemeulebroucke, J 2023, Probabilistic Tissue Mapping for Tumor Segmentation and Infiltration Detection of Glioma. in S Bakas, U Baid, B Baheti, A Crimi, S Malec, M Pytlarz, M Zenk & R Dorent (eds), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 8th International Workshop, BrainLes 2022, Held in Conjunction with MICCAI 2022, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13769 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 80-89, Proceedings of the 8th International MICCAI Brainlesion Workshop, BrainLes 2022, Singapore, Singapore, 18/09/22. https://doi.org/10.1007/978-3-031-33842-7_7
De Sutter, S., Geens, W., Bossa, M., Vanbinst, A. M., Duerinck, J., & Vandemeulebroucke, J. (2023). Probabilistic Tissue Mapping for Tumor Segmentation and Infiltration Detection of Glioma. In S. Bakas, U. Baid, B. Baheti, A. Crimi, S. Malec, M. Pytlarz, M. Zenk, & R. Dorent (Eds.), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 8th International Workshop, BrainLes 2022, Held in Conjunction with MICCAI 2022, Revised Selected Papers (pp. 80-89). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13769 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-33842-7_7
@inproceedings{3b5b112d3e5147fa95053030bdb786b7,
title = "Probabilistic Tissue Mapping for Tumor Segmentation and Infiltration Detection of Glioma",
abstract = "Segmentation of glioma structures is vital for therapy planning. Although state of the art algorithms achieve impressive results when compared to ground-truth manual delineations, one could argue that the binary nature of these labels does not properly reflect the underlying biology, nor does it account for uncertainties in the predicted segmentations. Moreover, the tumor infiltration beyond the contrast-enhanced lesion – visually imperceptible on imaging – is often ignored despite its potential role in tumor recurrence. We propose an intensity-based probabilistic model for brain tissue mapping based on conventional MRI sequences. We evaluated its value in the binary segmentation of the tumor and its subregions, and in the visualisation of possible infiltration. The model achieves a median Dice of 0.82 in the detection of the whole tumor, but suffers from confusion between different subregions. Preliminary results for the tumor probability maps encourage further investigation of the model regarding infiltration detection.",
keywords = "Glioma, Infiltration, Magnetic resonance imaging (MRI), Probabilistic, Segmentation",
author = "{De Sutter}, Selene and Wietse Geens and Mat{\'i}as Bossa and Vanbinst, {Anne Marie} and Johnny Duerinck and Jef Vandemeulebroucke",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; Proceedings of the 8th International MICCAI Brainlesion Workshop, BrainLes 2022 ; Conference date: 18-09-2022 Through 22-09-2022",
year = "2023",
doi = "10.1007/978-3-031-33842-7_7",
language = "English",
isbn = "9783031338410",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "80--89",
editor = "Spyridon Bakas and Ujjwal Baid and Bhakti Baheti and Alessandro Crimi and Sylwia Malec and Monika Pytlarz and Maximilian Zenk and Reuben Dorent",
booktitle = "Brainlesion",
address = "Germany",
}