Expectation-Maximization Regularised Deep Learning for Tumour Segmentation

Chao Li1; Wenjian Huang2; Xi Chen3; Yiran Wei1; Lipei Zhang4; Jianguo Zhang2; Stephen Price1; Carola-Bibiane Schönlieb4

10.1109/ISBI53787.2023.10230573

2023

1945-7928

978-1-6654-7359-0

2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)

2023-April

1-5

18-21 April 2023

Cartagena, Colombia

We present an expectation-maximization (EM) regularized deep learning (EMReDL) approach for weakly supervised tumor segmentation using partially labelled MRI. The proposed framework is demonstrated on glioblastoma, characterized by diffusion infiltration. Physiological MRI provides specific information regarding infiltration over structural MRI but is hindered by its low resolution for precise labeling. To exploit partial labels, we design two components in EMReDL: 1) a physiological prior prediction model: a neural network-based binary classifier trained by the labels of core tumor and normal-appearing regions. The trained classifier generates a physiological prior map passed to 2) a segmentation model regularized under an EM framework for weakly supervised learning. We evaluate the performance on a dataset with preoperative multiparametric and recurrence MRI. Results show that EMReDL can effectively segment the infiltrated tumor from the partially labeled MRI, with an accuracy higher than the model trained without physiological MRI and other competing approaches. We will publish the code with example data soon.

Expectation Maximization Weakly Supervised Learning Tumor Segmentation.

其他

CPCI-S ; EI

WOS:001062050500250

20233914806171

Deep learning ; Maximum principle ; Medical imaging ; Physiological models ; Physiology ; Supervised learning

Biomedical Engineering:461.1 ; Biological Materials and Tissue Engineering:461.2 ; Ergonomics and Human Factors Engineering:461.4 ; Biology:461.9 ; Imaging Techniques:746

IEEE

1.Department of Clinical Neuroscience, University of Cambridge
2.Department of Computer Science and Engineering, Southern University of Science and Technology
3.Department of Computer Science, University of Bath
4.Department of Applied Mathematics and Theoretical Physics, University of Cambridge

Chao Li,Wenjian Huang,Xi Chen,et al. Expectation-Maximization Regularised Deep Learning for Tumour Segmentation[C],2023:1-5.