Cahyo Dheo A Y, Wong Damon W K, Yow Ai Ping, Saw Seang-Mei, Schmetterer Leopold
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1286-1289. doi: 10.1109/EMBC44109.2020.9176184.
Many ocular diseases are associated with choroidal changes. Therefore, it is crucial to be able to segment the choroid to study its properties. Previous methods for choroidal segmentation have focused on single cross-sectional scans. Volumetric choroidal segmentation has yet to be widely reported. In this paper, we propose a sequential segmentation approach using a variation of U-Net with a bidirectional C-LSTM(Convolutional Long Short Term Memory) module in the bottleneck region. The model is evaluated on volumetric scans from 40 high myopia subjects, obtained using SS-OCT(Swept Source Optical Coherence Tomography). A comparison with other U-Net-based variants is also presented. The results demonstrate that volumetric segmentation of the choroid can be achieved with an accuracy of IoU(Intersection over Union) 0.92.Clinical relevance- This deep learning approach can automatically segment the choroidal volume, which can enable better evaluation and monitoring at ocular diseases.
许多眼部疾病都与脉络膜变化有关。因此，能够分割脉络膜以研究其特性至关重要。以前的脉络膜分割方法主要集中在单一横截面扫描上。体积脉络膜分割尚未得到广泛报道。在本文中，我们提出了一种顺序分割方法，该方法在瓶颈区域使用带有双向C-LSTM（卷积长短期记忆）模块的U-Net变体。该模型在使用扫频光学相干断层扫描（SS-OCT）获得的40名高度近视受试者的体积扫描上进行评估。还与其他基于U-Net的变体进行了比较。结果表明，脉络膜的体积分割可以达到交并比（IoU）为0.92的准确率。临床意义——这种深度学习方法可以自动分割脉络膜体积，从而能够更好地评估和监测眼部疾病。
