BACKGROUND
Accurate nuclei segmentation in histopathology images plays a key role in digital pathology. 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 intra-class 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, robust, and require less human effort, can be used. Recently, several AI-based nuclei 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 nuclei segmentation technique in which we adopt a new nuclei segmentation network empowered by residual skip connections to address this issue.
OBJECTIVE
The aim of this study was to develop an AI-based nuclei segmentation method for histopathology images of multiple organs.
METHODS
Our proposed residual-skip-connections-based nuclei segmentation network (R-NSN) is comprised of two main stages: Stain normalization and nuclei segmentation as shown in Figure 2. In the 1st stage, a histopathology image is stain normalized to balance the color and intensity variation. Subsequently, it is used as an input to the R-NSN in stage 2, which outputs a segmented image.
RESULTS
Experiments were performed on two publicly available datasets: 1) The Cancer Genomic 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 the 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.
CONCLUSIONS
The proposed R-NSN has the potential to maintain crucial features by using the residual connectivity from the encoder to the decoder and uses only a few layers, which reduces the computational cost of the model. The selection of a good stain normalization technique, the effective use of residual connections to avoid information loss, and the use of only a few layers to reduce the computational cost yielded outstanding results. Thus, our nuclei segmentation method is robust and is superior to the state-of-the-art methods. We expect that this study will contribute to the development of computational pathology software for research and clinical use and enhance the impact of computational pathology.
Precision Agriculture Technology: A Literature Review
