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Machine Learning for Health Informatics pp 125–148Cite as

Deep Learning Trends for Focal Brain Pathology Segmentation in MRI

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9605))

Abstract

Segmentation of focal (localized) brain pathologies such as brain tumors and brain lesions caused by multiple sclerosis and ischemic strokes are necessary for medical diagnosis, surgical planning and disease development as well as other applications such as tractography. Over the years, attempts have been made to automate this process for both clinical and research reasons. In this regard, machine learning methods have long been a focus of attention. Over the past two years, the medical imaging field has seen a rise in the use of a particular branch of machine learning commonly known as deep learning. In the non-medical computer vision world, deep learning based methods have obtained state-of-the-art results on many datasets. Recent studies in computer aided diagnostics have shown deep learning methods (and especially convolutional neural networks - CNN) to yield promising results. In this chapter, we provide a survey of CNN methods applied to medical imaging with a focus on brain pathology segmentation. In particular, we discuss their characteristic peculiarities and their specific configuration and adjustments that are best suited to segment medical images. We also underline the intrinsic differences deep learning methods have with other machine learning methods.

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Notes

  1. 1.

    Note that the BRATS organizers released a dataset in 2014 but quickly removed it from the web. This version of the dataset is no longer available.

  2. 2.

    Using stride of n means that every n pixel will be mapped to 1 pixel in the label map (assuming the model has one layer). This causes the model to loose pixel level accuracy if full image prediction is to be used at test time. One way to deal with this issue is presented by Pinheiro et al. [62]. Alternatively we can use stride of 1 every where in the model.

  3. 3.

    In the literature this way of up sampling is some times wrongly referred to as deconvolution.

  4. 4.

    Valid mode is when kernel and input have complete overlap.

  5. 5.

    Full mode is when minimum overlap is a sufficient condition for applying convolution.

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Authors and Affiliations

  1. Université de Sherbrooke, Sherbrooke, Canada

    Mohammad Havaei, Hugo Larochelle & Pierre-Marc Jodoin

  2. Imagia Inc., Montreal, Canada

    Nicolas Guizard

  3. Imeka Inc., Sherbrooke, Canada

    Pierre-Marc Jodoin

  4. Twitter Inc., Cambridge, USA

    Hugo Larochelle

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Correspondence to Mohammad Havaei .

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  1. Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz, Austria

    Andreas Holzinger

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Havaei, M., Guizard, N., Larochelle, H., Jodoin, PM. (2016). Deep Learning Trends for Focal Brain Pathology Segmentation in MRI. In: Holzinger, A. (eds) Machine Learning for Health Informatics. Lecture Notes in Computer Science(), vol 9605. Springer, Cham. https://doi.org/10.1007/978-3-319-50478-0_6

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