Abstract
Quantitative analysis of histopathological sections can be used to support the diagnosis and evaluate the disease progression by pathologists. The use of computer-aided diagnosis in pathology can substantially enhance the efficiency and accuracy of pathologists decisions, and overall benefit the patient. The evaluation of the shape of specific types of cell nuclei plays an important role in histopathological examination in various types of cancer. In this study we try to verify how much the results of segmentation could be improved with applying boundary refinement algorithm to thresholded histopathological image. In this paper we studied 5 methods based on various approaches: active contour, k-means clustering, and region-growing. For evaluation purposes, ground truth templates were generated by manual annotation of images. The performance is evaluated using pixel-wise sensitivity and specificity metrics. It appears that satisfactory results were achieved only by two algorithms based on active contour. By applying methodology based on active contour algorithm we managed to achieve sensitivity of about 93% and specificity of over 99%. To sum up, thresholding algorithms produce results that almost never perfectly fit to real object’s boundary, but this initial detection of objects followed by boundary refinement results in more accurate segmentation.
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We acknowledge the financial support of the Polish National Science Center grant, PRELUDIUM, 2013/11/N/ST7/02797.
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Roszkowiak, Ł., Korzyńska, A., Siemion, K., Pijanowska, D. (2019). The Influence of Object Refining in Digital Pathology. In: Choraś, M., Choraś, R. (eds) Image Processing and Communications Challenges 10. IP&C 2018. Advances in Intelligent Systems and Computing, vol 892. Springer, Cham. https://doi.org/10.1007/978-3-030-03658-4_7
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DOI: https://doi.org/10.1007/978-3-030-03658-4_7
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