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Open AccessArticle

Investigating the Behaviour of Machine Learning Techniques to Segment Brain Metastases in Radiation Therapy Planning

1
Department of Theoretical and Applied Science, University of Insubria, 21100 Varese, Italy
2
Department of Physics, University of Milan, 20122 Milan, Italy
3
C.S. Health Physics, ASST dei Sette Laghi, 21100 Varese, Italy
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(16), 3335; https://doi.org/10.3390/app9163335
Received: 15 July 2019 / Revised: 6 August 2019 / Accepted: 9 August 2019 / Published: 14 August 2019
(This article belongs to the Special Issue Machine Learning for Biomedical Data Analysis)
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Abstract

This work aimed to investigate whether automated classifiers belonging to feature-based and deep learning may approach brain metastases segmentation successfully. Support Vector Machine and V-Net Convolutional Neural Network are selected as representatives of the two approaches. In the experiments, we consider several configurations of the two methods to segment brain metastases on contrast-enhanced T1-weighted magnetic resonance images. Performances were evaluated and compared under critical conditions imposed by the clinical radiotherapy domain, using in-house dataset and public dataset created for the Multimodal Brain Tumour Image Segmentation (BraTS) challenge. Our results showed that the feature-based and the deep network approaches are promising for the segmentation of Magnetic Resonance Imaging (MRI) brain metastases achieving both an acceptable level of performance. Experimental results also highlight different behaviour between the two methods. Support vector machine (SVM) improves performance with a smaller training set, but it is unable to manage a high level of heterogeneity in the data and requires post-processing refinement stages. The V-Net model shows good performances when trained on multiple heterogeneous cases but requires data augmentations and transfer learning procedures to optimise its behaviour. The paper illustrates a software package implementing an integrated set of procedures for active support in segmenting brain metastases within the radiotherapy workflow. View Full-Text
Keywords: MRI brain segmentation; brain metastases; machine learning; features extraction; convolutional neural network; medical software MRI brain segmentation; brain metastases; machine learning; features extraction; convolutional neural network; medical software
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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 (CC BY 4.0).
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Gonella, G.; Binaghi, E.; Nocera, P.; Mordacchini, C. Investigating the Behaviour of Machine Learning Techniques to Segment Brain Metastases in Radiation Therapy Planning. Appl. Sci. 2019, 9, 3335.

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