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Open Access

ARTICLE

DenT: Dense-Transformer for Label-Free Microscopy Image Segmentation

Chan-Min Hsu1, Shang-Ru Yang1, Yi-Ju Lee1, An-Chi Wei1,2,*
1 Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
2 Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan
* Corresponding Author: An-Chi Wei. Email: email
(This article belongs to the Special Issue: Advancements in Pattern Recognition through Machine Learning: Bridging Innovation and Application)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.076098

Received 14 November 2025; Accepted 06 March 2026; Published online 01 April 2026

Abstract

U-Net, a fully convolutional neural network (FCNN) with U-shaped features, has demonstrated significant success in biomedical image segmentation. However, the locality of convolution operations in the U-Net limits its ability to learn long-range dependencies. Transformers, originally developed for natural language processing, have recently been adapted for image segmentation because of their global self-attention mechanisms. Inspired by the long-range feature learning capability of transformers, we propose Dense-Transformer (DenT), an architecture designed for volumetric microscopy image segmentation. DenT incorporates transformers as encoders within each convolutional layer to capture global contextual information. Additionally, dense skip connections at multiple resolutions enhance feature propagation, enabling precise localization. We evaluated DenT on mitochondrial segmentation using our confocal microscopy dataset and a public fluorescence microscope dataset from the Allen Institute for Cell Science. The experimental results demonstrate that DenT incrementally improves the segmentation of mitochondria and mitochondrial DNA substructures from transmitted light microscopy images. DenT offers a tool for visualization, measurement, and analysis of mitochondrial morphology and mitochondrial DNA in label-free microscopy.

Keywords

UNet; transformer; image segmentation; mitochondrial organelle; deep learning; microscopy imaging

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