Accurately segmenting fluid in 3D volumetric optical coherence tomography (OCT) images is a crucial yet challenging task for detecting eye diseases. Traditional autoencoding-based segmentation approaches have limitations in extracting fluid regions due to successive resolution loss in the encoding phase and the inability to recover lost information in the decoding phase. Although current transformer-based models for medical image segmentation addresses this limitation, they are not designed to be applied out-of-the-box for 3D OCT volumes, which have a wide-ranging channel-axis size based on different vendor device and extraction technique. To address these issues, we propose SwinVFTR, a new transformer-based architecture designed for precise fluid segmentation in 3D volumetric OCT images. We first utilize a channel-wise volumetric sampling for training on OCT volumes with varying depths (B-scans). Next, the model uses a novel shifted window transformer block in the encoder to achieve better localization and segmentation of fluid regions. Additionally, we propose a new volumetric attention block for spatial and depth-wise attention, which improves upon traditional residual skip connections. Consequently, utilizing multi-class dice loss, the proposed architecture outperforms other existing architectures on the three publicly available vendor-specific OCT datasets, namely Spectralis, Cirrus, and Topcon, with mean dice scores of 0.72, 0.59, and 0.68, respectively. Additionally, SwinVFTR outperforms other architectures in two additional relevant metrics, mean intersection-over-union (Mean-IOU) and structural similarity measure (SSIM).

翻译：在3D体素光学相干断层扫描（OCT）图像中精确分割液体区域是检测眼科疾病的关键但具有挑战性的任务。传统的基于自编码器的分割方法因编码阶段连续分辨率损失以及解码阶段无法恢复丢失信息，在提取液体区域方面存在局限性。尽管当前基于Transformer的医学图像分割模型解决了这一局限，但它们并非为直接应用于3D OCT体素而设计——不同厂商设备和提取技术会导致通道轴尺寸差异显著。针对这些问题，我们提出SwinVFTR，一种专为3D体素OCT图像精确液体分割设计的新型Transformer架构。首先，我们采用通道维体素采样策略，对具有不同深度（B扫描层数）的OCT体素进行训练。其次，模型在编码器中引入新型移位窗口Transformer模块，以实现对液体区域更精准的定位与分割。此外，我们提出一种新型体素注意力模块，用于空间与深度维度注意力机制，改进了传统残差跳跃连接。最终，通过多类别Dice损失函数，该架构在三个公开的可变厂商专用OCT数据集（Spectralis、Cirrus与Topcon）上均优于现有架构，平均Dice系数分别达到0.72、0.59和0.68。同时，SwinVFTR在平均交并比（Mean-IOU）和结构相似性度量（SSIM）两项额外相关指标上也表现出色。