Article ; Online: NRTR: Neuron Reconstruction With Transformer From 3D Optical Microscopy Images.
IEEE transactions on medical imaging
2024 Volume 43, Issue 2, Page(s) 886–898
Abstract: The neuron reconstruction from raw Optical Microscopy (OM) image stacks is the basis of neuroscience. Manual annotation and semi-automatic neuron tracing algorithms are time-consuming and inefficient. Existing deep learning neuron reconstruction methods, ...
| Abstract | The neuron reconstruction from raw Optical Microscopy (OM) image stacks is the basis of neuroscience. Manual annotation and semi-automatic neuron tracing algorithms are time-consuming and inefficient. Existing deep learning neuron reconstruction methods, although demonstrating exemplary performance, greatly demand complex rule-based components. Therefore, a crucial challenge is designing an end-to-end neuron reconstruction method that makes the overall framework simpler and model training easier. We propose a Neuron Reconstruction Transformer (NRTR) that, discarding the complex rule-based components, views neuron reconstruction as a direct set-prediction problem. To the best of our knowledge, NRTR is the first image-to-set deep learning model for end-to-end neuron reconstruction. The overall pipeline consists of the CNN backbone, Transformer encoder-decoder, and connectivity construction module. NRTR generates a point set representing neuron morphological characteristics for raw neuron images. The relationships among the points are established through connectivity construction. The point set is saved as a standard SWC file. In experiments using the BigNeuron and VISoR-40 datasets, NRTR achieves excellent neuron reconstruction results for comprehensive benchmarks and outperforms competitive baselines. Results of extensive experiments indicate that NRTR is effective at showing that neuron reconstruction is viewed as a set-prediction problem, which makes end-to-end model training available. |
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| MeSH term(s) | Microscopy ; Brain ; Neurons ; Algorithms ; Imaging, Three-Dimensional/methods ; Image Processing, Computer-Assisted |
| Language | English |
| Publishing date | 2024-02-02 |
| Publishing country | United States |
| Document type | Journal Article |
| ZDB-ID | 622531-7 |
| ISSN | 1558-254X ; 0278-0062 |
| ISSN (online) | 1558-254X |
| ISSN | 0278-0062 |
| DOI | 10.1109/TMI.2023.3323466 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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