# Cell recognition based on topological sparse coding for microscopy imaging of focused ultrasound treatment

## TL;DR

This paper introduces a new method for cell recognition in microscopy images using topological sparse coding, which improves the accuracy of identifying cell phases after ultrasound treatment.

## Contribution

The novelty lies in incorporating topological continuity into sparse coding for enhanced cell recognition in ultrasound-treated microscopy images.

## Key findings

- The proposed method outperforms SIFT, GIST, and HoG in sensitivity, specificity, F1 score, and accuracy.
- The topological sparse coding technique effectively extracts new features for cell recognition in MDA-MB-231 cell line images.
- RAW features are more suitable for deep learning in topological sparse coding than traditional feature extraction methods.

## Abstract

Ultrasound is considered a reliable, widely available, non-invasive, and inexpensive imaging technique for assessing and detecting the development phases of cancer; both in vivo and ex vivo, and for understanding the effects on cell cycle and viability after ultrasound treatment.

Based on the topological continuity characteristics, and that adjacent points or areas represent similar features, we propose a topological penalized convex objective function of sparse coding, to recognize similar cell phases.

This method introduces new features using a deep learning method of sparse coding with topological continuity characteristics. Large-scale comparison tests demonstrate that the RAW can outperform SIFT GIST and HoG as the input features with this method, achieving higher sensitivity, specificity, F1 score, and accuracy.

Experimental results show that the proposed topological sparse coding technique is valid and effective for extracting new features, and the proposed system was effective for cell recognition of microscopy images of theMDA-MB-231 cell line. This method allows features from sparse coding learning methods to have topological continuity characteristics, and the RAW features are more applicable for the deep learning of the topological sparse coding method than SIFT GIST and HoG.