Invariant Delineation of Nuclear Architecture in Glioblastoma Multiforme for Clinical and Molecular Association

IEEE transactions on medical imaging 12/2012; DOI: 10.1109/TMI.2012.2231420
Source: PubMed


Automated analysis of whole mount tissue sections can provide insights into tumor subtypes and the underlying molecular basis of neoplasm. However, since tumor sections are collected from different laboratories, inherent technical and biological variations impede analysis for very large datasets such as The Cancer Genome Atlas (TCGA). Our objective is to characterize tumor histopathology, through the delineation of the nuclear regions, from hematoxylin and eosin (H\E) stained tissue sections. Such a representation can then be mined for intrinsic subtypes across a large dataset for prediction and molecular association. Furthermore, nuclear segmentation is formulated within a multi-reference graph framework with geodesic constraints, which enables computation of multidimensional representations, on a cell-by-cell basis, for functional enrichment and bioinformatics analysis. Here, we present a novel method, multi-reference graph cut (MRGC), for nuclear segmentation that overcomes technical variations associated with sample preparation by incorporating prior knowledge from manually annotated reference images and local image features. The proposed approach has been validated on manually annotated samples and then applied to a dataset of 377 Glioblastoma Multiforme (GBM) whole slide images from 146 patients. For the GBM cohort, multidimensional representation of the nuclear features and their organization have identified 1) statistically significant subtypes based on several morphometric indexes, 2) whether each subtype can be predictive or not, and 3) that the molecular correlates of predictive subtypes are consistent with the literature.

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    • "Several outstanding reviews for the histology sections analysis can be found in [2] [3]. From our perspective, four distinct works have defined the trends in tissue histology analysis: (i) one group of researchers proposed nuclear segmentation and organization for tumor grading and/or the prediction of tumor recurrence [4] [5] [6] [7] [8]. (ii) A second group of researchers focused on patch level analysis (e.g., small regions) based on either human engineered features [9] [10] or features from unsupervised learning [11] [12] [13], for tumor representation . "
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    ABSTRACT: Classification of histology sections from large cohorts, in terms of distinct regions of microanatomy (e.g., tumor, stroma, normal), enables the quantification of tumor composition, and the construction of predictive models of the clinical outcome. To tackle the batch effects and biological heterogeneities that are persistent in large co-horts, sparse cellular morphometric context has recently been developed for invariant representation of the underlying properties in the data, which summarizes cellular morphometric features at various locations and scales, and leads to a system with superior performance for classification of microanatomy and histopathology. However , the sparse optimization protocol for the calculation of sparse cellular morphometric features is not scalable for large scale classification. To improve the scalability of systems, based on sparse mor-phometric context, we propose the predictive sparse morphometric context in place of the original implementation, which approximates the sparse cellular morphometric feature through a non-linear re-gressor that is jointly learned with an over-complete dictionary in an unsupervised manner. Experimental results indicates over 50 times speedup compared to our previous implementation with the help of non-linear regressor; while producing competitive performance.
    IEEE Int. Symp. on Biomedical Imaging: from nano to macro; 04/2015
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    • "Our method outperforms traditional nuclei segmentation algorithms [20] [22] and is very competitive with one of the state-of-the-art algorithm [21]. Note that unlike the algorithm in [21], which is built upon human engineered prior knowledge. Proposed SCCR is a generic feature learning model and may be applicable to segmentation tasks of other tumor types. "
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    ABSTRACT: Automated profiling of nuclear architecture, in histology sections, can potentially help predict the clinical outcomes. However, the task is challenging as a result of nuclear pleomorphism and cellular states (e.g., cell fate, cell cycle), which are compounded by the batch effect (e.g., variations in fixation and staining). Present methods, for nuclear segmentation, are based on human-designed features that may not effectively capture intrinsic nuclear architecture. In this paper, we propose a novel approach, called sparsity constrained convolutional regression (SCCR), for nuclei segmentation. Specifically, given raw image patches and the corresponding annotated binary masks, our algorithm jointly learns a bank of convolutional filters and a sparse linear regressor, where the former is used for feature extraction, and the latter aims to produce a likelihood for each pixel being nuclear region or background. During classification, the pixel label is simply determined by a thresholding operation applied on the likelihood map. The method has been evaluated using the benchmark dataset collected from The Cancer Genome Atlas (TCGA). Experimental results demonstrate that our method outperforms traditional nuclei segmentation algorithms and is able to achieve competitive performance compared to the state-of-the-art algorithm built upon human-designed features with biological prior knowledge.
    International Symposium on Biomedical Imaging: from nano to macro, Brooklyn, NY; 04/2015
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    • "The binarization of nuclei based on GC is addressed in [23] with results being more accurate than global thresholding. Prior knowledge like nucleus shape [24], manual annotation and local image features [25] can be incorporated in the GC framework to allow more robust seg- mentation. "
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