Image Analysis Based Grading of Bladder Carcinoma. Comparison of Object, Texture and Graph Based Methods and Their Reproducibility

Choi, Heung‐Kook; Jarkrans, Torsten; Bengtsson, Ewert; Vaskó, János; Wester, Kenneth; Malmström, Per‐Uno; Busch, Christer

doi:10.1155/1997/147187

Cited by 41 publications

(37 citation statements)

References 33 publications

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“…Several multivariate machine learning-based classification methods have been used for this purpose. Some of these include neural networks (37), nearest neighbors (38), logistic regression (39), linear discriminant analysis (40), decision trees (41), support vector machines (42) and others. In addition, voting-based strategies for combining the output of several different classifiers have been designed (43–47).…”

Section: Approaches For Automated Image-based Pathologymentioning

confidence: 99%

Detection and classification of thyroid follicular lesions based on nuclear structure from histopathology images

2010

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Follicular lesions of the thyroid are traditionally difficult and tedious challenges in diagnostic surgical pathology in part due to lack of obvious discriminatory cytological and microarchitectural features. We describe a computerized method to detect and classify follicular adenoma of the thyroid, follicular carcinoma of the thyroid, and normal thyroid based on the nuclear chromatin distribution from digital images of tissue obtained by routine histological methods. Our method is based on determining whether a set of nuclei, obtained from histological images using automated image segmentation, is most similar to sets of nuclei obtained from normal or diseased tissues. This comparison is performed utilizing numerical features, a support vector machine, and a simple voting strategy. We also describe novel methods to identify unique and defining chromatin patterns pertaining to each class. Unlike previous attempts in detecting and classifying these thyroid lesions using computational imaging, our results show that our method can automatically classify the data pertaining to 10 different human cases with 100% accuracy after blind cross validation using at most 43 nuclei randomly selected from each patient. We conclude that nuclear structure alone contains enough information to automatically classify the normal thyroid, follicular carcinoma, and follicular adenoma, as long as groups of nuclei (instead of individual ones) are used. We also conclude that the distribution of nuclear size and chromatin concentration (how tightly packed it is) seem to be discriminating features between nuclei of follicular adenoma, follicular carcinoma, and normal thyroid. ' 2010 International Society for Advancement of Cytometry Key terms follicular; thyroid; image analysis; nuclear structure; classification THE distinction between follicular adenoma of the thyroid (FA; neoplastic, but not capable of metastases) and follicular carcinoma of the thyroid (FTC; neoplastic, capable of metastases) has largely been a tedious and difficult task for pathologists and until very recently pathologists have only been able to rely on the histopathology to distinguish them. The encompassing differential diagnosis of follicular lesions of the thyroid includes follicular adenoma, follicular carcinoma, follicular variant of papillary carcinoma, and nodular goiter. Follicular variant of papillary carcinoma is usually not as troublesome as the distinction between FA and FTC due to the presence of distinctive and unique nuclear features in this variant of papillary carcinoma. The microscopic presence of tumor invasion into the capsule (fibrous covering around the lesion) and/or invasion of the tumor into small vessels of the capsule (vascular invasion) are the distinguishing hallmarks of FTC. FA may have a fibrous capsule, but lacks capsular and/or vascular invasion. These two features are diagnostic because other visual features commonly used by pathologists including microarchitecture and cytological characteristics, notably nuclear and cytoplasm...

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Section: Approaches For Automated Image-based Pathologymentioning

confidence: 99%

Detection and classification of thyroid follicular lesions based on nuclear structure from histopathology images

2010

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“…The most relevant work to the cell-graph approach is a Delaunay graph of cells obtained from a processed tissue image to compute graph-theoretical features for tissue quantification [28–30]. In this approach, nuclei are the vertices, the Voronoi diagram of the image is constituted, and its Delaunay triangulation is built.…”

Section: Discussionmentioning

confidence: 99%

Quantification of Spatial Parameters in 3D Cellular Constructs Using Graph Theory

Lund

Bilgin

Hasan

et al. 2009

BioMed Research International

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Multispectral three-dimensional (3D) imaging provides spatial information for biological structures that cannot be measured by traditional methods. This work presents a method of tracking 3D biological structures to quantify changes over time using graph theory. Cell-graphs were generated based on the pairwise distances, in 3D-Euclidean space, between nuclei during collagen I gel compaction. From these graphs quantitative features are extracted that measure both the global topography and the frequently occurring local structures of the “tissue constructs.” The feature trends can be controlled by manipulating compaction through cell density and are significant when compared to random graphs. This work presents a novel methodology to track a simple 3D biological event and quantitatively analyze the underlying structural change. Further application of this method will allow for the study of complex biological problems that require the quantification of temporal-spatial information in 3D and establish a new paradigm in understanding structure-function relationships.

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“…We have previously applied computer-based image analysis technique for malignancy grading of urinary bladder carcinoma with promising results regarding reproducibility (Jarkrans et al, 1995;Choi et al, 1997). In this study, we used a similar methodological approach to assess MVD.…”

Section: mentioning

confidence: 99%

Automatic quantification of microvessel density in urinary bladder carcinoma

et al. 1999

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Seventy-three TUR-T biopsies from bladder carcinoma were evaluated regarding microvessel density, defined as microvessel number (nMVD) and cross-section endothelial cell area (aMVD). A semi-automatic and a newly developed, automatic image analysis technique were applied in immunostainings, performed according to an optimized staining protocol. In 12 cases a comparison of biopsy material and the corresponding cystectomy specimen were tested, showing a good correlation in 11 of 12 cases (92%). The techniques proved reproducible for both nMVD and aMVD quantifications related to total tumour area. However, the automatic method was dependent on high immunostaining quality. Simultaneous, semi-automatic quantification of microvessels, stroma and epithelial fraction resulted in a decreased reproducibility. Quantification in ten images, selected in a descending order of MVD by subjective visual judgement, showed a poor observer capacity to estimate and rank MVD. Based on our results we propose quantification of MVD related to one tissue compartment. When staining quality is of high standard, automatic quantification is applicable, which facilitates quantification of multiple areas and thus, should minimize selection variability. © 1999 Cancer Research Campaign

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Image Analysis Based Grading of Bladder Carcinoma. Comparison of Object, Texture and Graph Based Methods and Their Reproducibility

Cited by 41 publications

References 33 publications

Detection and classification of thyroid follicular lesions based on nuclear structure from histopathology images

Detection and classification of thyroid follicular lesions based on nuclear structure from histopathology images

Quantification of Spatial Parameters in 3D Cellular Constructs Using Graph Theory

Automatic quantification of microvessel density in urinary bladder carcinoma

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