Automated segmentation of villi in histopathology images of placenta

S Salsabili; A Mukherjee; E Ukwatta; A D C Chan; S Bainbridge; D Grynspan

doi:10.1016/j.compbiomed.2019.103420

Automated segmentation of villi in histopathology images of placenta

Comput Biol Med. 2019 Oct:113:103420. doi: 10.1016/j.compbiomed.2019.103420. Epub 2019 Aug 30.

Authors

S Salsabili¹, A Mukherjee², E Ukwatta³, A D C Chan⁴, S Bainbridge⁵, D Grynspan⁶

Affiliations

¹ Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada. Electronic address: sina.salsabili@carleton.ca.
² Interdisciplinary School of Health Sciences, University of Ottawa, Ottawa, Ontario, Canada; Department of Pathology and Laboratory Medicine, Children's Hospital of Eastern Ontario, Ottawa, Ontario, Canada.
³ Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada; School of Engineering, University of Guelph, Guelph, Ontario, Canada.
⁴ Department of Systems and Computer Engineering, Carleton University, Ottawa, Ontario, Canada.
⁵ Department of Cellular and Molecular Medicine, University of Ottawa, Ontario, Canada; Interdisciplinary School of Health Sciences, University of Ottawa, Ontario, Canada.
⁶ Department of Pathology and Laboratory Medicine, Children's Hospital of Eastern Ontario and the University of Ottawa, Ontario, Canada.

PMID: 31514041
DOI: 10.1016/j.compbiomed.2019.103420

Abstract

Purpose: Manual analysis of clinical placenta pathology samples under the microscope is a costly and time-consuming task. Computer-aided diagnosis might offer a means to obtain fast and reliable results and also substantially reduce inter- and intra-rater variability. Here, we present a fully automated segmentation method that is capable of distinguishing the complex histological features of the human placenta (i.e., the chorionic villous structures).

Methods: The proposed pipeline consists of multiple steps to segment individual placental villi structures in hematoxylin and eosin (H&E) stained placental images. Artifacts and undesired objects in the histological field of view are detected and excluded from further analysis. One of the challenges in our new algorithm is the detection and segmentation of touching villi in our dataset. The proposed algorithm uses the top-hat transformation to detect candidate concavities in each structure, which might represent two distinct villous structures in close proximity. The detected concavities are classified by extracting multiple features from each candidate concavity. Our proposed pipeline is evaluated against manual segmentations, confirmed by an expert pathologist, on 12 scans from three healthy control patients and nine patients diagnosed with preeclampsia, containing nearly 5000 individual villi. The results of our method are compared to a previously published method for villi segmentation.

Results: Our algorithm detected placental villous structures with an F1 score of 80.76% and sensitivity of 82.18%. These values are substantially better than the previously published method, whose F1 score and sensitivity are 65.30% and 55.12%, respectively.

Conclusion: Our method is capable of distinguishing the complex histological features of the human placenta (i.e., the chorionic villous structures), removing artifacts over a large histopathology sample of human placenta, and (importantly) account for touching adjacent villi structures. Compared to existing methods, our developed method yielded high accuracy in detecting villi in placental images.

Keywords: Biomedical image processing; Color image processing; Histopathology; Image segmentation; Placenta; Preeclampsia.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adult
Algorithms*
Chorionic Villi* / metabolism
Chorionic Villi* / pathology
Female
Humans
Image Processing, Computer-Assisted*
Pre-Eclampsia* / metabolism
Pre-Eclampsia* / pathology
Pregnancy