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:3D 体积光学一致性断层成像(OCT) 图像的准确分解流流量对于检测眼病是一项至关重要但具有挑战性的任务。 传统的自动编码分解方法在提取流体区域方面有局限性, 原因是编码阶段连续的分辨率丢失, 在解码阶段无法恢复丢失的信息。 虽然基于变压器的医疗图象分解模型解决了这一限制, 但模型的设计并不是用于3D OCT 体积的盒子外应用, 3D OCT 体积基于不同的供应商设备和提取技术, 具有广泛的频道轴轴尺寸。 为了解决这些问题, 我们提议SwinVFTR, 一个新的基于变压器的架构, 用于3D体积体积 OCT 图像的精确分解分解。 我们首先使用一种基于频道的体积取样方法, 用于不同深度的 OCT 体积( B 扫描 ) 。 下一个模型使用新式的变换式窗口块块, 以更好地定位和分解流体积区域。 此外, 我们提出一个新的体积关注区块块块块块, 用于空间和深度的分解结构- 结构- 结构- 结构- 分流体积- 分流体积-, 分别使用S- c- sreal- c- c- sle- sal- sl- sld- sl- sal- sld- s</s>