Development of an image analysis workflow for the detection of cells in brain tumor slides using Machine Learning
Authors: Cevik Lokman , Aslan Mehmet Tahir , Thomas Diana , Otero Jose 
Published: April 2020

Background

Diagnosis of primary brain tumors requires an integrated evaluation of histologic, anatomic and molecular features. Accurate diagnosis is very important for prognosis and treatment. 2016 WHO classification of primary brain tumors required molecular methods for diagnosis of some diffuse gliomas. This increases the difficulty of diagnosis of diffuse gliomas especially for the poor countries and regions that have low availability of molecular tests.

The advent of whole slide imaging permits the usage of computer vision tools such as thresholding, deconvolution, and feature extraction. These developments make it possible for us to evaluate various morphological features in the neoplastic and normal cells. Our aim is to develop a Machine Learning (ML) model that is a low‐cost predictor of molecular test results from histomorphological information. To achieve this purpose, in this section we made a workflow for the detection of OLIG2 positive cells.

Methods

We used color deconvolution and k‐means clustering methods for the cell segmentation on the pictures of astrocytoma and oligodendroglioma slides stained with OLIG2. Then we extracted the morphological features of objects using the EBImage package. We classified the segmented objects as artifact, cell and fused for the ground truth of the classification. We divided the ground truth data into training (70%) and test data (30%). We applied Random Forest (RF) modeling as a machine learning algorithm for the prediction of the objects. We used Boruta function to identify feature importance. All data analysis and segmentation were done using R programming software.

Results

The deconvolution method for OLIG2 is apparently better than k‐means clustering. K‐means clustering failed to detect cell borders. RF model has high accuracy (88%) and high kappa value (0.8219) for the classification of objects. According to Boruta function, the most important features are features related to shape and pixel intensities. After Boruta selection, RF model has the same accuracy (88%) and lower kappa value (0.7715) for classification of objects (Table
1). We used the same workflow at the 55 brain tumor pictures. We detected 5837 cells in total of 12938 objects. In this section, we developed a successful workflow and we used this workflow for the detection of cells in OLIG2 stained digital slides.

Support or Funding Information

This study was supported by NIHR01HL132355

Confusion Matrix and Statistics

Reference

Prediction

Artifact

Cell

Fused

Artifact

352

52

2

Cell

74

750

30

Fused

15

13

156

Accuracy

0.8712

95% CI

(0.8528, 0.888)

No Information Rate

0.5644

P‐Value [Acc> NIR]

2.2e‐16

Kappa

0.7715

Mcnemar’s Test P‐Value

0.0001335

Artifact

Cell

Fused

Sensitivity

0.7982

0.9202

0.8298

Specificity

0.9462

0.8347

0.9777

Pos Pred Value

0.8670

0.8782

0.8478

Neg Pred Value

0.9143

0.8898

0.9746

Prevalence

0.3054

0.5644

0.1302

Detection Rate

0.2438

0.5194

0.1080

Detection Prevalence

0.2812

0.5914

0.1274

Balanced Accuracy

0.8722

0.8775

0.9037