Spine X-ray Image Segmentation Based on Transformer and Adaptive Optimized Postprocessing

Evaluation of Deep Neural Network Models for Instance Segmentation of Lumbar Spine MRI

“…In our study, we trained and compared a total of 15 models on our lumbar spine MRI dataset. These models are Attention U-Net (Oktay et al, 2018), HSNet (Zhang et al, 2022), Inception-SwinUnet (Pu et al, 2023), MedT (Valanarasu et al, 2021), MultiResUNet (Ibtehaz and Rahman, 2020), SLT-Net (Feng et al, 2022), Swin-Unet (Cao et al, 2023), UNETR (Hatamizadeh et al, 2021), Swin UNETR (Hatamizadeh et al, 2022), TransUNet (Chen et al, 2021), UCTransNet (Wang et al, 2022), UNet++ (Zhou et al, 2018), UNeXt (Valanarasu and Patel, 2022), UTNet (Gao et al, 2021), and BianqueNet (Zheng et al, 2022). We were unable to test all models mentioned in Section 2 due to either unavailability (e.g., no source code) or incompatibility (e.g., size not matching).…”

“…Medical Transformer (MedT) uses gated-axial Transformer layers in the encoder of a Unet (Valanarasu et al, 2021). HSNet used PVTv2 (Wang et al, 2022) as encoder and a dual-branch structure which Transformer branch and CNN branch fused by element-wise product as decoder for polyp segmentation (Zhang et al, 2022).…”

“…To the best of our knowledge, there are only a few Transformer-based networks (You et al, 2022; Tao et al, 2022) specifically designed for lumbar spine image segmentation, including EG-Trans3DUNet (You et al, 2022), Spine-transformers (Tao et al, 2022), APSegmenter (Zhang et al, 2022), and BianqueNet (Zheng et al, 2022). However, it is worth noting that most of these networks were developed for the segmentation of vertebral bodies using CT modality, which may not be directly applicable to the segmentation of the intervertebral discs in order to study disc degeneration.…”

“…The source code of the second stage is not publicly available. APSegmenter (Zhang et al, 2022) combines a ViT-style Transformer with a mask Transformer to segment spine X-ray images, and an adaptive postprocessing is applied to further refine the result. BianqueNet (Zheng et al, 2022) employed a resnet101 network to perform feature extraction, followed by upsampling using the Swin Transformer-skip connection module and a double upsampling operations.…”

“…UTNet introduced the 2-dimensional relative position encoding by adding relative position logits along height and width dimensions( R height , R width ) into the key embedding (Gao et al, 2021). HSNet and UCTransNet do not include position embedding in their models (Zhang et al, 2022; Wang et al, 2022)…”