Accuracy Improvement of Cell Image Segmentation Using Feedback Former

TL;DR

This paper introduces Feedback Former, a novel Transformer-based architecture with feedback mechanisms that enhances cell image segmentation accuracy by incorporating detailed information, outperforming existing methods in precision and efficiency.

Contribution

The paper proposes a feedback mechanism integrated into Transformer encoders for cell image segmentation, improving detail preservation and accuracy without increasing model size.

Findings

Feedback Former surpasses non-feedback methods in segmentation accuracy.

The method achieves higher precision with lower computational cost.

It outperforms conventional feedback approaches in accuracy and efficiency.

Abstract

Semantic segmentation of microscopy cell images by deep learning is a significant technique. We considered that the Transformers, which have recently outperformed CNNs in image recognition, could also be improved and developed for cell image segmentation. Transformers tend to focus more on contextual information than on detailed information. This tendency leads to a lack of detailed information for segmentation. Therefore, to supplement or reinforce the missing detailed information, we hypothesized that feedback processing in the human visual cortex should be effective. Our proposed Feedback Former is a novel architecture for semantic segmentation, in which Transformers is used as an encoder and has a feedback processing mechanism. Feature maps with detailed information are fed back to the lower layers from near the output of the model to compensate for the lack of detailed information which is the weakness of Transformers and improve the segmentation accuracy. By experiments on three cell image datasets, we confirmed that our method surpasses methods without feedback, demonstrating its superior accuracy in cell image segmentation. Our method achieved higher segmentation accuracy while consuming less computational cost than conventional feedback approaches. Moreover, our method offered superior precision without simply increasing the model size of Transformer encoder, demonstrating higher accuracy with lower computational cost.