Comput. Biol. Med. - Automatic differential diagnosis of pancreatic serous and mucinous cystadenomas based on morphological features.

Tópicos

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Resumo

Generally, pathological diagnosis using an electron microscope is time-consuming and likely to result in a subjective judgment, because pathologists perform manual screening of tissue slides at high magnifications. Recently, the advent of digital pathology technology has provided the basis for convenient screening and quantitative analysis by digitizing tissue slides through a computer system. However, a screening process with high magnification still takes quite a long time. To solve these problems, recently the use of computer-aided design techniques for performing pathologic diagnosis has been increasing in digital pathology. For pathological diagnosis, we need different diagnostic methods for different regions with different characteristics. Therefore, in order to effectively diagnose different lesions and types of diseases, a quantitative method for extracting specific features is required in computerized pathologic diagnosis. This study is about an automated differential diagnosis system to differentiate between benign serous cystadenoma and possibly-malignant mucinous cystadenoma. In order to diagnose cystic tumors, the first step is identifying a cystic region and inspecting its epithelial cells. First, we identify the lumen boundary of a cyst using the Direction Cumulative Map considering 8-ways. Then, the Epithelial Nuclei Identification algorithm is used to discern epithelial nuclei. After that, three morphological features for the differential diagnosis of mucinous and serous cystadenomas are extracted. To demonstrate the superiority of the proposed features, the experiments compared performance of the classifiers learned by using the proposed morphological features and the classical morphological features based on nuclei. The classifiers in the simulations are as follows; Bayesian Classifier, k-Nearest Neighbors, Support Vector Machine, and Artificial Neural Network. The results show that all classifiers using the proposed features have the best classification performance.

Resumo Limpo

general patholog diagnosi use electron microscop timeconsum like result subject judgment pathologist perform manual screen tissu slide high magnif recent advent digit patholog technolog provid basi conveni screen quantit analysi digit tissu slide comput system howev screen process high magnif still take quit long time solv problem recent use computeraid design techniqu perform patholog diagnosi increas digit patholog patholog diagnosi need differ diagnost method differ region differ characterist therefor order effect diagnos differ lesion type diseas quantit method extract specif featur requir computer patholog diagnosi studi autom differenti diagnosi system differenti benign serous cystadenoma possiblymalign mucin cystadenoma order diagnos cystic tumor first step identifi cystic region inspect epitheli cell first identifi lumen boundari cyst use direct cumul map consid way epitheli nuclei identif algorithm use discern epitheli nuclei three morpholog featur differenti diagnosi mucin serous cystadenoma extract demonstr superior propos featur experi compar perform classifi learn use propos morpholog featur classic morpholog featur base nuclei classifi simul follow bayesian classifi knearest neighbor support vector machin artifici neural network result show classifi use propos featur best classif perform

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