Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

MR-Class: A Python Tool for Brain Mr Image Classification Utilizing One-Vs-All DCNNS to Deal With the Open-Set Recognition Problem

Patrick Salome
* ,
Francesco Sforazzini
,
Gianluca Grugnara
,
Andreas Kudak
ORCID logo ,
Matthias Dostal
ORCID logo ,
Christel Herold-Mende
ORCID logo ,
Sabine Heiland
,
Jürgen Debus
,
Amir Abdollahi
,
Maximilian Knoll
*
Version 1 : Received: 6 January 2023 / Approved: 9 January 2023 / Online: 9 January 2023 (10:59:31 CET)

A peer-reviewed article of this Preprint also exists.

Salome, P.; Sforazzini, F.; Grugnara, G.; Kudak, A.; Dostal, M.; Herold-Mende, C.; Heiland, S.; Debus, J.; Abdollahi, A.; Knoll, M. MR-Class: A Python Tool for Brain MR Image Classification Utilizing One-vs-All DCNNs to Deal with the Open-Set Recognition Problem. Cancers 2023, 15, 1820. Salome, P.; Sforazzini, F.; Grugnara, G.; Kudak, A.; Dostal, M.; Herold-Mende, C.; Heiland, S.; Debus, J.; Abdollahi, A.; Knoll, M. MR-Class: A Python Tool for Brain MR Image Classification Utilizing One-vs-All DCNNs to Deal with the Open-Set Recognition Problem. Cancers 2023, 15, 1820.

Abstract

Background: MR image classification in datasets collected from multiple sources is complicated by inconsistent and missing DICOM metadata. Therefore, we aimed to establish a method for the efficient automatic classification of MR brain sequences. Methods: Deep convolutional neural networks (DCNN) were trained as one-vs-all classifiers to differentiate between six classes, T1 weighted (w), contrast-enhanced T1w, T2w, T2w-FLAIR, ADC, and SWI. Each classifier yields a probability, allowing threshold-based and relative probability assignment while excluding images with low probability (label: unknown, open-set recognition problem). Data from three high-grade glioma (HGG) cohorts was assessed; C1 (320 patients, 20101 MRI images) was used for training, while C2 (197, 11333) and C3 (256, 3522) were for testing. Two raters manually checked images through an interactive labeling tool. Finally, MR-Class' added value was evaluated via radiomics models' performance for progression-free survival (PFS) prediction in C2, utilizing the concordance index (C-I). Results: Approximately 10% of annotation errors were observed in each cohort between the DICOM series descriptions and the derived labels. MR-Class accuracy was 96.7% [95%-Cl: 95.8, 97.3] for C2 and 94.4% [93.6, 96.1] for C3. 620 images were misclassified; Manual assessment of those frequently showed motion artifacts or alterations of anatomy by large tumors. Implementation of MR-Class increased on average the PFS model C-I by 14.6% compared to a model trained without MR-Class. Conclusions: We provide a DCNN-based method for sequence classification of brain MR images and demonstrate its usability in two independent HGG datasets.

Keywords

Content-based image classification; Data curation and preparation; Convolutional neural networks (CNN); Deep learning; Artificial intelligence (AI)

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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