Abstract：
OBJECTIVE: Most recently transformer models became the state of the art in various medical image segmentation tasks and challenges, outperforming most of the conventional deep learning approaches. Picking up on that trend, this study aims at applying various transformer models to the highly challenging task of colorectal cancer (CRC) segmentation in CT imaging and assessing how they hold up to the current state-of-the-art convolutional neural network (CNN), the nnUnet. Furthermore, we wanted to investigate the impact of the network size on the resulting accuracies, since transformer models tend to be significantly larger than conventional network architectures.
METHODS: For this purpose, six different transformer models, with specific architectural advancements and network sizes were implemented alongside the aforementioned nnUnet and were applied to the CRC segmentation task of the medical segmentation decathlon.
RESULTS: The best results were achieved with the Swin-UNETR, D-Former, and VT-Unet, each transformer models, with a Dice similarity coefficient (DSC) of 0.60, 0.59 and 0.59, respectively. Therefore, the current state-of-the-art CNN, the nnUnet could be outperformed by transformer architectures regarding this task. Furthermore, a comparison with the inter-observer variability (IOV) of approx. 0.64 DSC indicates almost expert-level accuracy. The comparatively low IOV emphasizes the complexity and challenge of CRC segmentation, as well as indicating limitations regarding the achievable segmentation accuracy.
CONCLUSIONS: As a result of this study, transformer models underline their current upward trend in producing state-of-the-art results also for the challenging task of CRC segmentation. However, with ever smaller advances in total accuracies, as demonstrated in this study by the on par performances of multiple network variants, other advantages like efficiency, low computation demands, or ease of adaption to new tasks become more and more relevant.
METHODS: For this purpose, six different transformer models, with specific architectural advancements and network sizes were implemented alongside the aforementioned nnUnet and were applied to the CRC segmentation task of the medical segmentation decathlon.
RESULTS: The best results were achieved with the Swin-UNETR, D-Former, and VT-Unet, each transformer models, with a Dice similarity coefficient (DSC) of 0.60, 0.59 and 0.59, respectively. Therefore, the current state-of-the-art CNN, the nnUnet could be outperformed by transformer architectures regarding this task. Furthermore, a comparison with the inter-observer variability (IOV) of approx. 0.64 DSC indicates almost expert-level accuracy. The comparatively low IOV emphasizes the complexity and challenge of CRC segmentation, as well as indicating limitations regarding the achievable segmentation accuracy.
CONCLUSIONS: As a result of this study, transformer models underline their current upward trend in producing state-of-the-art results also for the challenging task of CRC segmentation. However, with ever smaller advances in total accuracies, as demonstrated in this study by the on par performances of multiple network variants, other advantages like efficiency, low computation demands, or ease of adaption to new tasks become more and more relevant.
摘要：
目的：最近，在各种医学图像分割任务和挑战中，变压器模型成为最先进的技术，优于大多数传统的深度学习方法。注意到这一趋势，这项研究旨在将各种变压器模型应用于CT成像中的结直肠癌（CRC）分割这一极具挑战性的任务，并评估它们如何适应当前最先进的卷积神经网络（CNN）。nnUnet。此外，我们想调查网络规模对结果准确性的影响，因为变压器模型往往比传统的网络体系结构大得多。
方法：为此，六种不同的变压器型号，与上述nnUnet一起实现了特定的体系结构改进和网络规模，并将其应用于医学分段十项全能的CRC分段任务。
结果：使用Swin-UNETR取得了最好的结果，D-前,和VT-Unet,每个变压器型号，Dice相似系数(DSC)分别为0.60、0.59和0.59。因此,目前最先进的CNN，nnUnet可以通过变压器架构来执行此任务。此外，与约的观察者间变异性（IOV）的比较。0.64DSC显示几乎专家级的准确性。相对较低的IOV强调了CRC分割的复杂性和挑战性，以及指示有关可实现的分割精度的限制。
结论：作为这项研究的结果，变压器模型强调了他们目前的上升趋势，在生产状态的最先进的结果也为具有挑战性的任务CRC分割。然而,随着总准确度的进步越来越小，正如这项研究通过多个网络变体的同等性能所证明的那样，其他优势，如效率，低计算需求，或易于适应新任务变得越来越重要。
方法：为此，六种不同的变压器型号，与上述nnUnet一起实现了特定的体系结构改进和网络规模，并将其应用于医学分段十项全能的CRC分段任务。
结果：使用Swin-UNETR取得了最好的结果，D-前,和VT-Unet,每个变压器型号，Dice相似系数(DSC)分别为0.60、0.59和0.59。因此,目前最先进的CNN，nnUnet可以通过变压器架构来执行此任务。此外，与约的观察者间变异性（IOV）的比较。0.64DSC显示几乎专家级的准确性。相对较低的IOV强调了CRC分割的复杂性和挑战性，以及指示有关可实现的分割精度的限制。
结论：作为这项研究的结果，变压器模型强调了他们目前的上升趋势，在生产状态的最先进的结果也为具有挑战性的任务CRC分割。然而,随着总准确度的进步越来越小，正如这项研究通过多个网络变体的同等性能所证明的那样，其他优势，如效率，低计算需求，或易于适应新任务变得越来越重要。
