Automatic Brain Tumor Grading from MRI Data Using Convolutional Neural Networks and Quality Assessment

Pereira, Sérgio; Meier, Raphael; Alves, Victor; Reyes, Mauricio; Silva, Carlos A. (2018). Automatic Brain Tumor Grading from MRI Data Using Convolutional Neural Networks and Quality Assessment. In: Understanding and Interpreting Machine Learning in Medical Image Computing Applications. Lecture Notes in Computer Science: Vol. 11038 (pp. 106-114). Cham: Springer International Publishing 10.1007/978-3-030-02628-8_12

[img] Text
PereiraiMIMIC2018.pdf - Accepted Version
Restricted to registered users only
Available under License Publisher holds Copyright.

Download (540kB) | Request a copy

Glioblastoma Multiforme is a high grade, very aggressive, brain tumor, with patients having a poor prognosis. Lower grade gliomas are less aggressive, but they can evolve into higher grade tumors over time. Patient management and treatment can vary considerably with tumor grade, ranging from tumor resection followed by a combined radio- and chemotherapy to a ``wait and see'' approach. Hence, tumor grading is important for adequate treatment planning and monitoring. The gold standard for tumor grading relies on histopathological diagnosis of biopsy specimens. However, this procedure is invasive, time consuming, and prone to sampling error. Given these disadvantages, automatic tumor grading from widely used MRI protocols would be clinically important, as a way to expedite treatment planning and assessment of tumor evolution. In this paper, we propose to use Convolutional Neural Networks for predicting tumor grade directly from imaging data. In this way, we overcome the need for expert annotations of regions of interest. We evaluate two prediction approaches: from the whole brain, and from an automatically defined tumor region. Finally, we employ interpretability methodologies as a quality assurance stage to check if the method is using image regions indicative of tumor grade for classification.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology
04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB [discontinued]

UniBE Contributor:

Meier, Raphael and Reyes, Mauricio

Subjects:

600 Technology > 610 Medicine & health
500 Science > 570 Life sciences; biology

ISBN:

978-3-030-02628-8

Series:

Lecture Notes in Computer Science

Publisher:

Springer International Publishing

Language:

English

Submitter:

Mauricio Antonio Reyes Aguirre

Date Deposited:

02 Oct 2019 17:53

Last Modified:

23 Oct 2019 11:44

Publisher DOI:

10.1007/978-3-030-02628-8_12

BORIS DOI:

10.7892/boris.132347

URI:

https://boris.unibe.ch/id/eprint/132347

Actions (login required)

Edit item Edit item
Provide Feedback