Transformer vs. CNN – A Comparison on Knee Segmentation in Ultrasound Images

Abstract

The automated and robust segmentation of bone surfaces in ultrasound (US) images can open up new fields of application for US imaging in computer-assisted orthopedic surgery, e.g. for the patient-specific planning process in computer-assisted knee replacement. For the automated, deep learning-based segmentation of medical images, CNN-based methods have been the state of the art over the last years, while recently Transformer-based methods are on the rise in computer vision. To compare these methods with respect to US image segmentation, in this paper the recent Transformer- based Swin-UNet is exemplarily benchmarked against the commonly used CNN-based nnUNet on the application of in-vivo 2D US knee segmentation. Trained and tested on our own dataset with 8166 annotated images (split in 7155 and 1011 images respectively), both the nnUNet and the pre-trained Swin-UNet show a Dice coefficient of 0.78 during testing. For distances between skeletonized labels and predictions, a symmetric Hausdorff distance of 44.69 pixels and a symmetric surface distance of 5.77 pixels is found for nnUNet as compared to 42.78 pixels and 5.68 pixels respectively for the Swin-UNet. Based on qualitative assessment, the Transformer-based Swin-UNet appears to benefit from its capability of learning global relationships as compared to the CNN-based nnUNet, while the latter shows more consistent and smooth predictions on a local level, presumably due to the character of convolution operation. Besides, the Swin-UNet requires generalized pre-training to be competitive. Since both architectures are evenly suited for the task at hand, for our future work, hybrid architectures combining the characteristic advantages of Transformer-based and CNN-based methods seem promising for US image segmentation.

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