A novel convolutional neural network for semantic segmentation of microscopic and retinal images

Abstract

Semantic segmentation of medical images remains a challenging task, particularly for microscopic cell images and retinal fundus photographs, where precise boundary delineation and structural preservation are critical. In this work, we tackle these two applications by proposing a new convolutional neural network based on U-Net with a structured depth schedule that concentrates representational capacity at the lower-resolution bottleneck phases, improving edge definition, region filling and structural detail recovery. Our model was evaluated on two benchmark datasets covering both application domains and compared against six established baselines. For microscopic cell segmentation, it achieved the best scores in PSNR, SSIM, Precision and F1-Score. For retinal vessel extraction, it led in SSIM and Recall while placing second in IoU and F1-Score, presenting the most balanced performance profile among all evaluated models. Qualitative results confirm sharper segmentation boundaries and finer structural details relative to all competing methods, including under challenging illumination and background conditions.