Learning the Topological Properties of Brain Tumors

Abstract

Abstract-This work presents a graph-based representation (a.k.a., cell-graph) of histopathological images for automated cancer diagnosis by probabilistically assigning a link between a pair of cells (or cell clusters). Since the node set of a cell-graph can include a cluster of cells as well as individual ones, it enables working with low-cost, low-magnification photomicrographs. The contributions of this work are twofold. First, it is shown that without establishing a pairwise spatial relation between the cells (i.e., the edges of a cellgraph), neither the spatial distribution of the cells nor the texture analysis of the images yields accurate results for tissue level diagnosis of brain cancer called malignant glioma. Second, this work defines a set of global metrics by processing the entire cell-graph to capture tissue level information coded into the histopathological images. In this work, the results are obtained on the photomicrographs of 646 archival brain biopsy samples of 60 different patients. It is shown that the global metrics of cell-graphs distinguish cancerous tissues from noncancerous ones with high accuracy (at least 99 percent accuracy for healthy tissues with lower cellular density level, and at least 92 percent accuracy for benign tissues with similar high cellular density level such as nonneoplastic reactive/inflammatory conditions).

Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection

“…In our calculation, we do not consider the pixels that could be included in either side (either cancerous or normal region) without affecting the medical interpretation (the pixels that are shaded in red in Figs. [5][6][7][8]. We report the average and the standard deviation of the sensitivity, specificity, and accuracy percentages in Table 1.…”

“…However, as it mainly relies on the visual interpretation, this examination may lead to a considerable amount of subjectivity, especially in cancer grading [1,2]. To reduce this subjectivity, it has been proposed to use computational methods that rely on the quantification of a tissue by defining mathematical features [3][4][5][6][7]. Although the very first step in this quantification is the segmentation of a tissue image into homogeneous regions, these studies have not mainly focused on this problem and have extracted features from the tissue image assuming that it is homogeneous.…”

Mathematical modeling of the malignancy of cancer using graph evolution

“…In this work, we prefer this prob-ability function, which is also used in one of our earlier work [15], since it has a single control parameter so that we could control the graph connectivity by only this single parameter. In this function, the probability of being an edge between two nodes decays with the increasing Euclidean distance between these two nodes.…”

“…In our previous studies [14][15][16], we propose to use cell-graphs for the purpose of cancer diagnosis. In those studies, we construct simple [14,15] and weighted [16] graphs from tissues and compute their local and global graph metrics in order to distinguish cancerous tissues (regardless of their malignancy grades) from non-cancerous ones.…”

“…In those studies, we construct simple [14,15] and weighted [16] graphs from tissues and compute their local and global graph metrics in order to distinguish cancerous tissues (regardless of their malignancy grades) from non-cancerous ones. As opposed to our previous studies, this work proposes a novel computational model based on the use of graph evolution process for the purpose of cancer grading.…”