Meeting Banner
Abstract #2154

Incorporating Histological Grading for Brain Tumor Segmentation

Lipei Zhang1, Yiran We2, Chao Li1,2, Stephen John Price2, and Carola Bibiane Schönlieb1
1The Centre of Mathematical Imaging in Healthcare, Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom, 2Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Deoartment of Clinic Neuroscience, Univerisity of Cambridge, Cambridge, United Kingdom


Automatic tumor segmentation on MRI is crucial for patient management of brain tumors. However, previous automatic MRI segmentation methods based on deep learning are limited by bottlenecks of feature extraction. In this paper, we seek to investigate the influence of incorporating prior histology grading on the performance of tumor segmentation. We propose to employ histological grade labels to strengthen the feature extraction and tumor localization in the encoder, which can boost the DSC at exceeding 2% in different U-Net baselines. Our results could support the usefulness of incorporating clinical prior knowledge to improve the segmentation performance without introducing extra parameters.

This abstract and the presentation materials are available to members only; a login is required.

Join Here