Mask RCNN algorithm for nuclei detection on breast cancer histopathological images

Nuclei detection is a key step in computer assisted pathology. Due to the variability of the size, shape, appearance, and texture of breast cancer nuclei in histopathological images, automated nuclei detection has always been a difficult aspect of computer‐aided pathology research. In this article,...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International journal of imaging systems and technology 2022-01, Vol.32 (1), p.209-217
Hauptverfasser: Huang, Hui, Feng, Xi'an, Jiang, Jionghui, Chen, Peiyu, Zhou, Suying
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Nuclei detection is a key step in computer assisted pathology. Due to the variability of the size, shape, appearance, and texture of breast cancer nuclei in histopathological images, automated nuclei detection has always been a difficult aspect of computer‐aided pathology research. In this article, Mask RCNN is presented for the automatic detection of nuclei on high‐resolution histopathological images of breast cancer. Mask RCNN uses the ResNet network and effectively combines modules such as feature pyramid networks (FPN), ROIAlign, and fully convolutional networks (FCN). FPN can efficiently extract features of various dimensions in images. ROIAlign can improve the accuracy of the detection model in the detection task. FCN renders the prediction results more detailed. The experiment results show that the application of this algorithm is superior to other algorithms in terms of its intuitive vision, as well as in performance indicators such as accuracy, recall, and F‐Measure.
ISSN:0899-9457
1098-1098
DOI:10.1002/ima.22618