KEYWORDS: Image segmentation, 3D modeling, Retina, Image processing algorithms and systems, Detection and tracking algorithms, Signal to noise ratio, 3D image processing, Image contrast enhancement, Medical image reconstruction, Medical image processing
Purpose: Spectral Domain Optical Coherence Tomography (SD-OCT) is a much utilized imaging modality in retina clinics to inspect the integrity of retinal layers in patients with age related macular degeneration. Spectralis and Cirrus are two of the most widely used SD-OCT vendors. Due to the stark difference in intensities and signal to noise ratio’s between the images captured by the two instruments, a model trained on images from one instrument performs poorly on the images of the other instrument. Methods: In this work, we explore the performance of an algorithm trained on images obtained from the Heidelberg Spectralis device on Cirrus images. Utilizing a dataset containing Heidelberg images and Cirrus images, we address the problem of accurately segmenting images on one domain with an algorithm developed on another domain. In our approach we use unpaired CycleGAN based domain adaptation network to transform the Cirrus volumes to the Spectralis volumes, before using our trained segmentation network. Results: We show that the intensity distribution shifts towards the Spectralis domain when we domain adapt Cirrus images to Spectralis images. Our results show that the segmentation model performs significantly better on the domain translated volumes (Total Retinal Volume Error: 0.17±0.27mm3, RPEDC Volume Error: 0.047±0.05mm3) compared to the raw volumes (Total Retinal VolumeError: 0.26±0.36mm3, RPEDC Volume Error: 0.13±0.15mm3) from the Cirrus domain and that such domain adaptation approaches are feasible solutions. Conclusions: Both our qualitative and quantitative results show that CycleGAN domain adaptation network can be used as an efficient technique to perform unpaired domain adaptation between SD-OCT images generated from different devices. We show that a 3D segmentation model trained on Spectralis volume performs better on domain adapted Cirrus volumes, compared to raw Cirrus volumes.
Purpose: Spectral Domain Optical Coherence Tomography (SD-OCT) images are a series of Bscans which capture the volume of the retina and reveal structural information. Diseases of the outer retina cause changes to the retinal layers which are evident on SD-OCT images, revealing disease etiology and risk factors for disease progression. Quantitative thickness information of the retina layers provide disease relevant data that reveal important aspects of disease pathogenesis. Manually labeling these layers is extremely laborious, time consuming and costly. Recently, deep learning algorithms have been used for automating the process of segmentation. While retinal volumes are inherently 3 dimensional, state-of-the-art segmentation approaches have been limited in their utilization of the 3 dimensional nature of the structural information. Methods: In this work, we train a 3D-UNet using 150 retinal volumes and test using 191 retinal volumes from a hold out test set (with AMD severity grade ranging from no disease through the intermediate stages to the advanced disease, and presence of geographic atrophy). The 3D deep features learned by the model captures spatial information simultaneously from all the three volumetric dimensions. Since unlike the ground truth, the output of 3D-UNet is not single pixel wide, we perform a column wise probabilistic maximum operation to obtain single pixel wide layers, for quantitative evaluations. Results: We compare our results to the publicly available OCT Explorer and deep learning based 2D-UNet algorithms and observe a low error within 3.11 pixels with respect to the ground truth locations (for some of the most challenging or advanced stage AMD eyes with AMD severity score: 9 and 10). Conclusion: Our results show that both qualitatively and quantitatively there is a significant advantage of extracting and utilizing 3D features over the traditionally used OCT Explorer or 2D-UNet.
Retinal toxicity among long-term users of Hydroxychloroquine manifests with loss in the Ellipsoid zone (EZ) detectable on SD-OCT imaging. This work reports an automatic deep-learning algorithm to detect and segment EZ loss in SD-OCT. The proposed model predicts EZ loss map, in a dual network architecture that operates in parallel combining scan-by-scan detections in horizontal and vertical directions. The combined model demonstrated the best overall performance with F1 score = 0.91 ± 0.07, improving the performance compared to individual models. Automatic methods for EZ loss detection could provide a useful tool to facilitate screening of patients for evidence of toxicity.
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