Deep-learning for thyroid microstructure segmentation in 2D OCT images

Iulian Emil Tampu; Anders Eklund; Neda Haj-Hosseini

doi:10.1117/12.2576854

5 March 2021 Deep-learning for thyroid microstructure segmentation in 2D OCT images

Iulian Emil Tampu, Anders Eklund, Neda Haj-Hosseini

Proceedings Volume 11630, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXV; 116301Z (2021) https://doi.org/10.1117/12.2576854
Event: SPIE BiOS, 2021, Online Only

Conference Poster

Abstract

Optical coherence tomography (OCT) can provide exquisite details of tissue microstructure without traditional tissue sectioning, with potential diagnostic and intraoperative applications in a variety of clinical areas. In thyroid surgery, OCT could provide information to reduce the risk of damaging normal tissue. Thyroid tissue's follicular structure alters in case of various pathologies including the non-malignant ones which can be imaged using OCT. The success of deep learning for medical image analysis encourages its application on OCT thyroid images for quantitative analysis of tissue microstructure. To investigate the potential of a deep learning approach to segment the follicular structure in OCT images, a 2D U-Net was trained on b-scan OCT images acquired from ex vivo adult human thyroid samples a effected by a range of pathologies. Results on a pool of 104 annotated images showed a mean Dice score of 0.74±0.19 and 0.92±0.09 when segmenting the follicular structure and the surrounding tissue on the test dataset (n=10 images). This study shows that a deep learning approach for tissue microstructure segmentation in OCT images is possible. The achieved performance without requiring manual intervention encourages the application of a deep-learning method for real-time analysis of OCT data.

Conference Presentation

Citation Download Citation

Iulian Emil Tampu, Anders Eklund, and Neda Haj-Hosseini "Deep-learning for thyroid microstructure segmentation in 2D OCT images", Proc. SPIE 11630, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXV, 116301Z (5 March 2021); https://doi.org/10.1117/12.2576854

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