AIM: To explore a more accurate quantifying diagnosis method of diabetic macular edema(DME) by displaying detailed 3D morphometry beyond the gold-standard quantification indicator-central retinal thickness(CRT) and ap...AIM: To explore a more accurate quantifying diagnosis method of diabetic macular edema(DME) by displaying detailed 3D morphometry beyond the gold-standard quantification indicator-central retinal thickness(CRT) and apply it in follow-up of DME patients.METHODS: Optical coherence tomography(OCT) scans of 229 eyes from 160 patients were collected.We manually annotated cystoid macular edema(CME), subretinal fluid(SRF) and fovea as ground truths.Deep convolution neural networks(DCNNs) were constructed including U-Net, sASPP, HRNetV2-W48, and HRNetV2-W48+Object-Contextual Representation(OCR) for fluid(CME+SRF) segmentation and fovea detection respectively, based on which the thickness maps of CME, SRF and retina were generated and divided by Early Treatment Diabetic Retinopathy Study(ETDRS) grid.RESULTS: In fluid segmentation, with the best DCNN constructed and loss function, the dice similarity coefficients(DSC) of segmentation reached 0.78(CME), 0.82(SRF), and 0.95(retina).In fovea detection, the average deviation between the predicted fovea and the ground truth reached 145.7±117.8 μm.The generated macular edema thickness maps are able to discover center-involved DME by intuitive morphometry and fluid volume, which is ignored by the traditional definition of CRT>250 μm.Thickness maps could also help to discover fluid above or below the fovea center ignored or underestimated by a single OCT B-scan.CONCLUSION: Compared to the traditional unidimensional indicator-CRT, 3D macular edema thickness maps are able to display more intuitive morphometry and detailed statistics of DME, supporting more accurate diagnoses and follow-up of DME patients.展开更多
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 l...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.展开更多
文摘AIM: To explore a more accurate quantifying diagnosis method of diabetic macular edema(DME) by displaying detailed 3D morphometry beyond the gold-standard quantification indicator-central retinal thickness(CRT) and apply it in follow-up of DME patients.METHODS: Optical coherence tomography(OCT) scans of 229 eyes from 160 patients were collected.We manually annotated cystoid macular edema(CME), subretinal fluid(SRF) and fovea as ground truths.Deep convolution neural networks(DCNNs) were constructed including U-Net, sASPP, HRNetV2-W48, and HRNetV2-W48+Object-Contextual Representation(OCR) for fluid(CME+SRF) segmentation and fovea detection respectively, based on which the thickness maps of CME, SRF and retina were generated and divided by Early Treatment Diabetic Retinopathy Study(ETDRS) grid.RESULTS: In fluid segmentation, with the best DCNN constructed and loss function, the dice similarity coefficients(DSC) of segmentation reached 0.78(CME), 0.82(SRF), and 0.95(retina).In fovea detection, the average deviation between the predicted fovea and the ground truth reached 145.7±117.8 μm.The generated macular edema thickness maps are able to discover center-involved DME by intuitive morphometry and fluid volume, which is ignored by the traditional definition of CRT>250 μm.Thickness maps could also help to discover fluid above or below the fovea center ignored or underestimated by a single OCT B-scan.CONCLUSION: Compared to the traditional unidimensional indicator-CRT, 3D macular edema thickness maps are able to display more intuitive morphometry and detailed statistics of DME, supporting more accurate diagnoses and follow-up of DME patients.
基金supported by the National Natural Science Foundation of China under Grant Nos.62301330 and 62101346the Guangdong Basic and Applied Basic Research Foundation under Grant Nos.20231121103807001,2022A1515110101the Guangdong Provincial Key Laboratory under Grant No.2023B1212060076.
文摘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.
