  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).
