Accurate segmentation of nuclear regions with multi-organ histopathology images using artificial intelligence for cancer diagnosis in personalized medicine

Tahir Mahmood, Muhammad Owais, Kyoung Jun Noh, Hyo Sik Yoon, Ja Hyung Koo, Adnan Haider, Haseeb Sultan, Kang Ryoung Park

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

Accurate nuclear segmentation in histopathology images plays a key role in digital pathol-ogy. It is considered a prerequisite for the determination of cell phenotype, nuclear morphometrics, cell classification, and the grading and prognosis of cancer. However, it is a very challenging task because of the different types of nuclei, large intraclass variations, and diverse cell morphologies. Consequently, the manual inspection of such images under high-resolution microscopes is tedious and time-consuming. Alternatively, artificial intelligence (AI)-based automated techniques, which are fast and robust, and require less human effort, can be used. Recently, several AI-based nuclear segmentation techniques have been proposed. They have shown a significant performance improvement for this task, but there is room for further improvement. Thus, we propose an AI-based nuclear segmentation technique in which we adopt a new nuclear segmentation network empowered by residual skip connections to address this issue. Experiments were performed on two publicly available datasets: (1) The Cancer Genome Atlas (TCGA), and (2) Triple-Negative Breast Cancer (TNBC). The results show that our proposed technique achieves an aggregated Jaccard index (AJI) of 0.6794, Dice coefficient of 0.8084, and F1-measure of 0.8547 on TCGA dataset, and an AJI of 0.7332, Dice coefficient of 0.8441, precision of 0.8352, recall of 0.8306, and F1-measure of 0.8329 on the TNBC dataset. These values are higher than those of the state-of-the-art methods.

Original languageEnglish
Article number515
JournalJournal of Personalized Medicine
Volume11
Issue number6
DOIs
StatePublished - Jun 2021

Keywords

  • Artificial intelligence
  • Cancer grading and prognosis
  • Multi-organ histopathology images
  • Nuclear segmentation
  • Stain normalization
  • The Cancer Genome Atlas
  • Triple-negative breast cancer

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