Automated discrimination of lower and higher grade gliomas based on histopathological image analysis

Abstract

Introduction: Histopathological images have rich structural information, are multi-channel in nature and contain meaningful pathological information at various scales. Sophisticated image analysis tools that can automatically extract discriminative information from the histopathology image slides for diagnosis remain an area of significant research activity. In this work, we focus on automated brain cancer grading, specifically glioma grading. Grading of a glioma is a highly important problem in pathology and is largely done manually by medical experts based on an examination of pathology slides (images). To complement the efforts of clinicians engaged in brain cancer diagnosis, we develop novel image processing algorithms and systems to automatically grade glioma tumor into two categories: Low-grade glioma (LGG) and high-grade glioma (HGG) which represent a more advanced stage of the disease.

Results: We propose novel image processing algorithms based on spatial domain analysis for glioma tumor grading that will complement the clinical interpretation of the tissue. The image processing techniques are developed in close collaboration with medical experts to mimic the visual cues that a clinician looks for in judging of the grade of the disease. Specifically, two algorithmic techniques are developed: (1) A cell segmentation and cell-count profile creation for identification of Pseudopalisading Necrosis, and (2) a customized operation of spatial and morphological filters to accurately identify microvascular proliferation (MVP). In both techniques, a hierarchical decision is made via a decision tree mechanism. If either Pseudopalisading Necrosis or MVP is found present in any part of the histopathology slide, the whole slide is identified as HGG, which is consistent with World Health Organization guidelines. Experimental results on the Cancer Genome Atlas database are presented in the form of: (1) Successful detection rates of pseudopalisading necrosis and MVP regions, (2) overall classification accuracy into LGG and HGG categories, and (3) receiver operating characteristic curves which can facilitate a desirable trade-off between HGG detection and false-alarm rates.

Conclusion: The proposed method demonstrates fairly high accuracy and compares favorably against best-known alternatives such as the state-of-the-art WND-CHARM feature set provided by NIH combined with powerful support vector machine classifier. Our results reveal that the proposed method can be beneficial to a clinician in effectively separating histopathology slides into LGG and HGG categories, particularly where the analysis of a large number of slides is needed. Our work also reveals that MVP regions are much harder to detect than Pseudopalisading Necrosis and increasing accuracy of automated image processing for MVP detection emerges as a significant future research direction.

Keywords: Brain cancer; cell segmentation; computer-aided diagnosis; decision tree; glioblastoma multiforme; low-grade glioma; micro-vascular proliferation; morphological transformation; pathological grading; pseudopalisading necrosis; spatial analysis; spatial filters.

Figure 1

Example of high-grade glioma (HGG) whole slide images (a) and a zoomed in view of the HGG tissue (b)

Figure 2

Example of low-grade glioma (LGG) whole slide images (a) and a zoomed in view of the LGG tissue (b)

Figure 3

Example images of brain tissue (a) low-grade glioma, (b) necrosis region and (c) microvascular proliferation as two main hallmarks of high-grade glioma

Figure 4

An example of flow of procedures in Necrosis detection algorithm. (a) Region of interest (b) Segmented image (c) Cell-count profile obtained from segmented image (d) Horizontal view of cell-count profile

Figure 5

An example of flow of procedures for MVP detection algorithm on a small ROI. (a) sample ROI (b) The output image after applying median filter (c) filtered image by mean filter (d) detected valley in the image showed as a red dot (e) segmented potential MVP region

Figure 6

Pipeline of procedures for each method

Figure 7

Decision Trees (a) Necrosis Decision Tree (b) MVP Decision Tree

Figure 8

Receiver Operating Characteristics (ROC) curves for different methods. (a) ROC curve of the proposed method for MVP detection (b) ROC curve of the proposed method for Necrosis detection (c) HGG vs. LGG ROC curve

Figure 9

Sensitivity of overall accuracy of the microvascular proliferation detection (a-c) necrosis detection (d) to the choice of parameters in the model. We deliberately chose the most influential parameters in the decision trees (s4, s8, s10, t4, t5) and evaluated the performance (overall classification accuracy) of each algorithm with respect to the change of these parameters. The white squares correspond to the higher overall accuracies while darker squares mean lower performance. The gray bar is showing the accuracies in percentage