Enhanced DeepLab Based Nerve Segmentation with Optimized Tuning

TL;DR

This paper introduces an optimized DeepLabV3-based method for nerve segmentation in ultrasound images, utilizing automated threshold tuning to enhance accuracy and outperform baseline models.

Contribution

It presents a novel pipeline that combines parameter optimization and refined preprocessing for improved nerve segmentation accuracy.

Findings

Dice Score of 0.78 achieved

IoU of 0.70 achieved

Pixel Accuracy of 0.95 achieved

Abstract

Nerve segmentation is crucial in medical imaging for precise identification of nerve structures. This study presents an optimized DeepLabV3-based segmentation pipeline that incorporates automated threshold fine-tuning to improve segmentation accuracy. By refining preprocessing steps and implementing parameter optimization, we achieved a Dice Score of 0.78, an IoU of 0.70, and a Pixel Accuracy of 0.95 on ultrasound nerve imaging. The results demonstrate significant improvements over baseline models and highlight the importance of tailored parameter selection in automated nerve detection.