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 contrastenhanced
lesion – visually imperceptible on imaging – is often ignored
despite its potential role in tumor recurrence. We propose an intensitybased
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.
Original languageEnglish
Title of host publicationBrainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
PublisherSpringer
Publication statusAccepted/In press - 2022

Fingerprint

Dive into the research topics of 'Probabilistic tissue mapping for tumor segmentation and infiltration detection of glioma'. Together they form a unique fingerprint.

Cite this