Efficient Multi-Organ Multi-Center Cell Nuclei Segmentation Method Based on Deep Learnable Aggregation Network

Abstract

Automated cell nuclei delineation in whole-slide imaging (WSI) is a fundamental step for many tasks like cancer cell recognition, cancer grading, and cancer subtype classification. Although numerous computational methods have been proposed for segmenting nuclei in WSI images based on image processing and deep learning, existing approaches face major challenges such as color variation due to the use of different stains, the various structures of cell nuclei, and the overlapping and clumped cell nuclei. To circumvent these challenges in this article, we propose an efficient and accurate cell nuclei segmentation method based on deep learning, in which a set of accurate individual cell nuclei segmentation models are developed to predict rough segmentation masks, and then a learnable aggregation network (LANet) is used to predict the final nuclei masks. Besides, we develop cell nuclei segmentation software (with a graphical user interface—GUI) that includes the proposed method and other deep-learning-based cell nuclei segmentation methods. A challenging WSI dataset collected from different centers and organs is used to demonstrate the efficiency of our method. The experimental results reveal that our method obtains a competitive performance compared to the existing approaches in terms of the aggregated Jaccard index (AJI=89.25%) and F1-score (F1=73.02%). The developed nuclei segmentation software can be downloaded from https://github.com/loaysh2010/Cell-Nuclei-Segmentation-GUI-Application.

A survey on recent trends in deep learning for nucleus segmentation from histopathology images

“…Second, it was challenging to distinguish nearby nuclei; they were treated as a single item. Third, many apps use whole-slide photos, which have dimensions far greater than 1000 × 1000 pixels Year: 2021 Hassan et al ( 2021b ) proposed cell nuclei segmentation method based on deep learning Features: Backbone: Not mentioned Loss: Cross-entropy Stochastic Gradient Descent (SGD) was used as an optimizer. The proposed work is intended to develop GUI-based cell nuclei segmentation software consisting of the proposed method along with other deep learning-based cell nuclei segmentation methods.…”

“…Hassan et al (2021b) proposed cell nuclei segmentation method based on deep learning Features: Backbone: Not mentioned Loss: Cross-entropy Stochastic Gradient Descent (SGD) was used as an optimizer. The proposed work is intended to develop GUI-based cell nuclei segmentation software consisting of the proposed method along with other deep learning-based cell nuclei segmentation methods.…”

Collaborative Colorectal Cancer Classification on Highly Class Imbalanced Data Setting via Federated Neural Style Transfer Based Data Augmentation

“…The manual analysis process of histopathological tissue samples has been shifted to the era of the digital pathology with the innovation of the digital scanners enabling the wholeslide images (WSI) which capture the image from glass slides as a whole [4]. More and more, the digitally acquired tissue images and the rise of the machine learning (ML) and deep learning (DL) algorithms give birth to a novel field called computational pathology (CP) [4][5][6]. In the ML field, as one of the most vibrant fields in academia and the industry, a new sub-branch called federated learning (FL) has been initiated by Google researchers in 2016 [7].…”