Machine Learning for Brain Disorders by Olivier Colliot

Author:Olivier Colliot
Language: eng
Format: epub
ISBN: 9781071631959
Publisher: Springer US

4.2 DL Algorithms for Brain WSI Analysis

In recent times, deep-learning-based methods have shown promising results in digital pathology [52]. Unfortunately, only a few public datasets contain WSI of brain tissue, and most of them only contain brain tumors. In addition, most of them are annotated at the slide level, making the semantic segmentation of structures more challenging. Independently of the task (i.e., detection/classification or segmentation) and the application in brain disorders, we will explore the main ideas behind the methodologies proposed in the literature.

For the analysis of benign or cancerous pathologies in brain tissue, tumor cell nuclei are of significant interest. The usual framework for analyzing such pathologies was reported in [53] and used the WSI of diffuse glioma. The method first segments the regions of interest by applying classical image processing techniques such as mathematical morphology and thresholding. Then, several handcrafted features such as nuclear morphometry, region texture, intensity, and gradient statistics were computed and inputted to a nuclei classifier. Although such an approach—using quadratic discriminant analysis and maximum a posteriori (MAP) as a classification mechanism—reported an overall accuracy of 87.43%, it falls short compared to CNN, which relies on automated feature extractions using convolutions rather than on handcrafted features. Xing et al. [54] proposed an automatic learning-based framework for robust nucleus segmentation. The method begins by dividing the image into small regions using a sliding window technique. These patches are then fed to a CNN to output probability maps and generate initial contours for the nuclei using a region merging algorithm. The correct nucleus segmentation is obtained by alternating dictionary-based shape deformation and inference. This method outperformed classical image processing algorithms with promising results (mean Dice similarity coefficient of 0.85 and detection F1 score of 0.77 computed using gold-standard regions within 15 pixels for every nucleus center) using CNN-based features over classical ones.

Following a similar approach, Xu et al. [55] reported the use of deep convolutional activation features for brain tumor classification and segmentation. The authors used a pre-trained AlexNet CNN [56] on the ImageNet dataset to extract patch features from the last hidden layer of the architecture. Features are then ranked based on the difference between the two classes of interest, and the top 100 are finally input to an SVM for classification. For the segmentation of necrotic tissue, an additional step involving probability mappings from SVM confidence scores and morphological smoothing is applied. Other approaches leveraging the use of CNN-based features for glioma are presented in [47, 57]. The experiments reported achieved a maximum accuracy of 97.5% for classification and 84% for segmentation. Although these results seemed promising, additional tests with different patch sizes in [47] suggested that the method’s performance is data-dependent as numbers increase when larger patches, meaning more context information, are used.

With the improvement of CNN architectures for natural images, more studies are also leveraging transfer learning to propose end-to-end methodologies for analyzing brain tumors. Ker et al. [58] used a pre-trained Google Inception V3 network to classify brain histology specimens into normal, low-grade glioma (LGG), or high-grade glioma (HGG).

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