Abstract
The accuracy of the applied technique for automated nuclei segmentation is critical in obtaining high-quality and efficient diagnostic results. Unfortunately, multiple objects in histopathological images are connected (clustered) and frequently counted as one. In this study, we present a new method for cluster splitting based on distance transform binarized with the recurrently increased threshold value and modified watershed algorithm. The proposed method treats clusters separately, splitting them into smaller sub-clusters and conclusively into separate objects, based solely on the shape feature, making it independent of the pixel intensity. The efficiency of these algorithms is validated based on the labeled set of images from two datasets: BBBC004v1 and breast cancer tissue microarrays. Results of initial nuclei detection were significantly improved by applying the proposed algorithms. Our approach outperformed the state-of-the-art techniques based on recall, precision, F1-score, and Jaccard index. The proposed method achieves very low amount of under-segmented, as well as over-segmented objects. In summary, we provide novel and efficient method for dividing the clustered nuclei in digital images of histopathological slides.
Highlights
In recent years, the development of computational pathology has strongly influenced the progress in quantitative digital pathology [1]
We present a new approach for cluster splitting, which is based on the distance transform and modified watershed algorithm
2 Materials and methods All classical image processing approach algorithms were implemented in MATLAB R2015b and are available at http://ibib.waw.pl/en/scientific-activity/projects/167-umo2013-11-n-st7-02797 and on Medical Image Analysis Platform (MIAP) [30] and MATLAB File Exchange
Summary
The development of computational pathology has strongly influenced the progress in quantitative digital pathology [1]. The availability of whole slide image technology has encouraged the development of many systems that perform computer-aided diagnosis. Certain obstacles and limitations still exist in achieving a reliable result. One of the primary problems is the overlapping of structures, called clusters of objects (commonly cell nuclei), that are segmented for quantification. The problem arises when multiple objects are counted as one due to clustering. Clustering is observed independently of the applied tissue stains, regardless of the use of hematoxylin and eosin (H&E) or different types of immunohistochemical (IHC) stains.
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