Central serous chorioretinopathy (CSCR) is a chorioretinal disorder of the eye characterized by serous detachment of the neurosensory retina at the posterior pole of the eye. CSCR results from the accumulation of subretinal fluid (SRF) due to idiopathic defects at the level of the retinal pigment epithelial (RPE) that allows serous fluid from the choriocapillaris to diffuse into the subretinal space between RPE and neurosensory retinal layers. This condition is presently investigated by clinicians using invasive angiography or non-invasive optical coherence tomography (OCT) imaging. OCT images provide a representation of the fluid underlying the retina, and in the absence of automated segmentation tools, currently only a qualitative assessment of the same is used to follow the progression of the disease. Automated segmentation of the SRF can prove to be extremely useful for the assessment of progression and for the timely management of CSCR. In this paper, we adopt an existing architecture called SegCaps, which is based on the recently introduced Capsule Networks concept, for the segmentation of SRF from CSCR OCT images. Furthermore, we propose an enhancement to SegCaps, which we have termed as DRIP-Caps, that utilizes the concepts of Dilation, Residual Connections, Inception Blocks, and Capsule Pooling to address the defined problem. The proposed model outperforms the benchmark UNet architecture while reducing the number of trainable parameters by 54.21%. Moreover, it reduces the computation complexity of SegCaps by reducing the number of trainable parameters by 37.85%, with competitive performance. The experiments demonstrate the generalizability of the proposed model, as evidenced by its remarkable performance even with a limited number of training samples.

中央浆液性脉络膜视网膜病变 (CSCR) 是一种眼部脉络膜视网膜疾病，其特征是眼后极的感觉神经视网膜浆液性脱离。CSCR 是由于视网膜色素上皮 (RPE) 水平的特发性缺陷导致视网膜下液 (SRF) 积聚所致，该缺陷允许来自脉络膜毛细血管的浆液扩散到 RPE 和神经感觉视网膜层之间的视网膜下空间。这种情况目前由临床医生使用侵入性血管造影术或非侵入性光学相干断层扫描 (OCT) 成像进行研究。OCT 图像提供了视网膜下方液体的表示，并且在没有自动分割工具的情况下，目前仅使用相同的定性评估来跟踪疾病的进展。SRF 的自动分割可以证明对于评估进展和及时管理 CSCR 非常有用。在本文中，我们采用一种名为 SegCaps 的现有架构，该架构基于最近引入的胶囊网络概念，用于从 CSCR OCT 图像中分割 SRF。此外，我们提出了对 SegCaps 的增强，我们称之为 DRIP-Caps，它利用膨胀、残差连接、初始块和胶囊池的概念来解决定义的问题。所提出的模型优于基准 UNet 架构，同时将可训练参数的数量减少了 54.21%。此外，它通过将可训练参数的数量减少 37.85% 来降低 SegCaps 的计算复杂度，具有竞争力的性能。