A Bayesian Learning Application to Automated Tumour Segmentation for Tissue Microarray Analysis

Tissue microarray (TMA) is a high throughput analysis tool to identify new diagnostic and prognostic markers in human cancers. However, standard automated method in tumour detection on routine histochemical images for TMA construction is under developed. This paper presents a MRF based Bayesian learning system for automated tumour cell detection in routine histochemical virtual slides to assist TMA construction. The experimental results show that the proposed method is able to achieve 80% accuracy on average by pixel-based quantitative performance evaluation that compares the automated segmentation outputs with the manually marked ground truth data. The presented technique greatly reduces labor-intensive workloads for pathologists, highly speeds up the process of TMA construction and allows further exploration of fully automated TMA analysis.

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