Automatic fovea detection and choroid segmentation for choroidal thickness assessment in optical coherence tomography

AIM:To develop an automated model for subfoveal choroidal thickness(SFCT)detection in optical coherence tomography(OCT)images,addressing manual fovea location and choroidal contour challenges.METHODS:Two procedures were proposed:defining the fovea and segmenting the choroid.Fovea localization from B-scan OCT image sequence with three-dimensional reconstruction(LocBscan-3D)predicted fovea location using central foveal depression features,and fovea localization from two-dimensional en-face OCT(LocEN-2D)used a mask region-based convolutional neural network(Mask R-CNN)model for optic disc detection,and determined the fovea location based on optic disc relative position.Choroid segmentation also employed Mask R-CNN.RESULTS:For 53 eyes in 28 healthy subjects,LocBscan-3D’s mean difference between manual and predicted fovea locations was 170.0μm,LocEN-2D yielded 675.9μm.LocEN-2D performed better in non-high myopia group(P=0.02).SFCT measurements from Mask R-CNN aligned with manual values.CONCLUSION:Our models accurately predict SFCT in OCT images.LocBscan-3D excels in precise fovea localization even with high myopia.LocEN-2D shows high detection rates but lower accuracy especially in the high myopia group.Combining both models offers a robust SFCT assessment approach,promising efficiency and accuracy for large-scale studies and clinical use.

A Transformer-Assisted Cascade Learning Network for Choroidal Vessel Segmentation

As a highly vascular eye part,the choroid is crucial in various eye disease diagnoses.However,limited research has focused on the inner structure of the choroid due to the challenges in obtaining sufficient accurate label data,particularly for the choroidal vessels.Meanwhile,the existing direct choroidal vessel segmentation methods for the intelligent diagnosis of vascular assisted ophthalmic diseases are still unsatisfactory due to noise data,while the synergistic segmentation methods compromise vessel segmentation performance for the choroid layer segmentation tasks.Common cascaded structures grapple with error propagation during training.To address these challenges,we propose a cascade learning segmentation method for the inner vessel structures of the choroid in this paper.Specifically,we propose TransformerAssisted Cascade Learning Network(TACLNet)for choroidal vessel segmentation,which comprises a two-stage training strategy:pre-training for choroid layer segmentation and joint training for choroid layer and choroidal vessel segmentation.We also enhance the skip connection structures by introducing a multi-scale subtraction connection module designated as MSC,capturing differential and detailed information simultaneously.Additionally,we implement an auxiliary Transformer branch named ATB to integrate global features into the segmentation process.Experimental results exhibit that our method achieves the state-of-the-art performance for choroidal vessel segmentation.Besides,we further validate the significant superiority of the proposed method for retinal fluid segmentation in optical coherence tomography(OCT)scans on a publicly available dataset.All these fully prove that our TACLNet contributes to the advancement of choroidal vessel segmentation and is of great significance for ophthalmic research and clinical application.