TY  - EJOU
AU  - Hsu, Chan-Min 
AU  - Yang, Shang-Ru 
AU  - Lee, Yi-Ju 
AU  - Wei, An-Chi 

TI  - DenT: Dense-Transformer for Label-Free Microscopy Image Segmentation
T2  - Computers, Materials \& Continua

PY  - 
VL  - 
IS  - 
SN  - 1546-2226

AB  - 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.
KW  - UNet; transformer; image segmentation; mitochondrial organelle; deep learning; microscopy imaging

DO  - 10.32604/cmc.2026.076098