TY - JOUR
T1 - Accurate segmentation of nuclear regions with multi-organ histopathology images using artificial intelligence for cancer diagnosis in personalized medicine
AU - Mahmood, Tahir
AU - Owais, Muhammad
AU - Noh, Kyoung Jun
AU - Yoon, Hyo Sik
AU - Koo, Ja Hyung
AU - Haider, Adnan
AU - Sultan, Haseeb
AU - Park, Kang Ryoung
N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/6
Y1 - 2021/6
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Cancer grading and prognosis
KW - Multi-organ histopathology images
KW - Nuclear segmentation
KW - Stain normalization
KW - The Cancer Genome Atlas
KW - Triple-negative breast cancer
UR - http://www.scopus.com/inward/record.url?scp=85108210121&partnerID=8YFLogxK
U2 - 10.3390/jpm11060515
DO - 10.3390/jpm11060515
M3 - Article
AN - SCOPUS:85108210121
SN - 2075-4426
VL - 11
JO - Journal of Personalized Medicine
JF - Journal of Personalized Medicine
IS - 6
M1 - 515
ER -