SpineDL: a Deep Learning-based approach for neuron andanatomical structure segmentation in immunofluorescenceimages of damaged spinal cords
Ruiz-Amezcua, P.; Franco-Barranco, D.; Reigada-Prado, D.; Munoz-Galdeano, T.; Martinez-Maza, R.; Nieto-Diaz, M.
Show abstract
In this study, we present SpineDL, an open-source deep learning (DL) approach for neurons and anatomical structure segmentation of the spinal cord in fluorescence images immunostained with NeuN and DAPI, within the context of murine models of spinal cord injury (SCI). SpineDL comprises two main modules: 1) SpineDL-Structure, for semantic segmentation of key spinal cord structures: gray matter, white matter, ependyma, and damaged tissue; and 2) SpineDL-Neuron, for instance-level identification of neuronal somas. To train the models, we developed the SpineDL dataset, a curated collection of 161 confocal images of mouse spinal cord, manually annotated by experts and organized into specific subsets. Both models are based on the HRNetV2-W48 architecture and were trained using state-of-the-art data augmentation and optimization techniques, implemented within the BiaPy framework, following an iterative refinement process driven by quantitative evaluation and expert feedback. Our results show that SpineDL achieves expert-level performance for both cases of structural segmentation and neuron identification. This work provides a robust, reproducible, and extensible platform for the spatial analysis of neurodegeneration following spinal cord injury, representing a step toward the automation of histopathological workflows in neuroscience.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- AnNoBrainer, an Automated Annotation of Mouse Brain Images using Deep Learning 97%
- Improved segmentation of the intracranial and ventricular volumes in populations with cerebrovascular lesions and atrophy using 3D CNNs 94%
- BrainLine: An Open Pipeline for Connectivity Analysis of Heterogeneous Whole-Brain Fluorescence Volumes 93%
Similar papers in this journal
Similar papers in this journal
- Fine-tuning TrailMap: The utility of transfer learning to improve the performance of deep learning in axon segmentation of light-sheet microscopy images 96%
- Semantic Segmentation of HeLa Cells: An Objective Comparison between one Traditional Algorithm and Three Deep-Learning Architectures 95%
- pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.