Efficient and fully-automatic retinal choroid segmentation in OCT through DL-based distillation of a hand-crafted pipeline
| Autor: | Burke, Jamie, Engelmann, Justin, Hamid, Charlene, Reid-Schachter, Megan, Pearson, Tom, Pugh, Dan, Dhaun, Neeraj, King, Stuart, MacGillivray, Tom, Bernabeu, Miguel O., Storkey, Amos, MacCormick, Ian J. C. |
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| Rok vydání: | 2023 |
| Předmět: |
FOS: Computer and information sciences
Artificial Intelligence (cs.AI) Computer Science - Artificial Intelligence FOS: Biological sciences Image and Video Processing (eess.IV) FOS: Electrical engineering electronic engineering information engineering Electrical Engineering and Systems Science - Image and Video Processing Quantitative Biology - Quantitative Methods Quantitative Methods (q-bio.QM) |
| DOI: | 10.48550/arxiv.2307.00904 |
| Popis: | Retinal vascular phenotypes, derived from low-cost, non-invasive retinal imaging, have been linked to systemic conditions such as cardio-, neuro- and reno-vascular disease. Recent high-resolution optical coherence tomography (OCT) allows imaging of the choroidal microvasculature which could provide more information about vascular health that complements the superficial retinal vessels, which current vascular phenotypes are based on. Segmentation of the choroid in OCT is a key step in quantifying choroidal parameters like thickness and area. Gaussian Process Edge Tracing (GPET) is a promising, clinically validated method for this. However, GPET is semi-automatic and thus requires time-consuming manual interventions by specifically trained personnel which introduces subjectivity and limits the potential for analysing larger datasets or deploying GPET into clinical practice. We introduce DeepGPET, which distils GPET into a neural network to yield a fully-automatic and efficient choroidal segmentation method. DeepGPET achieves excellent agreement with GPET on data from 3 clinical studies (AUC=0.9994, Dice=0.9664; Pearson correlation of 0.8908 for choroidal thickness and 0.9082 for choroidal area), while reducing the mean processing time per image from 34.49s ($\pm$15.09) to 1.25s ($\pm$0.10) on a standard laptop CPU and removing all manual interventions. DeepGPET will be made available for researchers upon publication. Comment: 11 pages, 2 figures, 3 tables. Currently in submission to the OMIA-X workshop as part of the 2023 MICCAI annual conference. GitHub link to codebase provided upon publication |
| Databáze: | OpenAIRE |
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