Saliency and ballness driven deep learning framework for cell segmentation in bright field microscopic images. (February 2023)

• Record Type: Journal Article
• Authors: Asha, S.B.; Gopakumar, G.; Subrahmanyam, Gorthi R.K. Sai
• Is Part Of: Engineering applications of artificial intelligence. Volume 118(2023)
• Publication Date: 2023-02
• Subjects: Deep learning based semantic segmentation -- Encoder–decoder model -- Microscopic image analysis -- Bright-field microscopy; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105704
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Cell segmentation is the most significant task in microscopic image analysis as it facilitates differential cell counting and analysis of sub-cellular structures for diagnosing cytopathological diseases. Bright-field microscopy is considered the gold standard among different types of optical microscopes used for cell analysis due to its simplicity and cost-effectiveness. However, automatic cell segmentation in bright field microscopy is challenging due to imaging artifacts, poor contrast, overlapping cells, and wide variability of cells. Also, the availability of labeled bright-field images is limited, further constraining the research in developing supervised models for automated cell segmentation. In this research, we propose a novel cell segmentation framework termed Saliency and Ballness driven U-shaped Network (SBU-net) to overcome these challenges. The proposed architecture comprises a novel data-driven feature fusion module that enhances the perceivable structure of cells using its saliency and ballness features. This, together with an encoder–decoder model having dilated convolutions and a novel combination loss function, captured the global context of cell structures and produced accurate cell segmentation results. SBU-net is evaluated using two publicly available bright-field datasets of T cells and pancreatic cancer cells. The model is subjected to 5-fold cross-validation and outperformed state-of-the-art models by producing mean Intersection over Union (IoU) scores of 0.804, 0.829, and mean Dice of 0.891, 0.906, respectively. The architecture was also tested on a fluorescent dataset to see how well it could generalize, and it came out with a mean IoU of 0.892 and a mean Dice of 0.948, outperforming other models reported in the literature.