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

Abstract

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...

Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles

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Detecting and visualizing cell phenotype differences from microscopy images using transport-based morphometry

“…The nuclear datasets were segmented as described in ref. 10. The liver dataset were segmented to have 500 nuclei with an average of 50 nuclei per patient.…”

“…The first application is concerned with discovering the principal differences in nuclear chromatin arrangement between normal, benign, and malignant cells extracted from the liver and thyroid of pediatric patients (10). Nuclear structure has long been a highly used biomarker in image-based pathology.…”

Histology Image Classification Using Supervised Classification and Multimodal Fusion

“…Another direction aims at performing a better segmentation of the image before further processing with the hope that the segmented objects (cell or nucleus) could represent the characteristics of the whole images better. Wang et al indicated that the classification accuracies could reach 100% using a simple nearest neighbor classification algorithm on testing instances after segmenting the nuclei region from the original image and extracting features of chromatin patterns based on the segmented image [18]. A similar work in [19] also showed that extracting features from the gland and nuclei areas which were segmented from the original images gave promising results in prostate cancer grading, breast cancer detection, and breast cancer grading.…”