论文标题
焦点：用于边界感知的CT图像的焦点变压器
FocalUNETR: A Focal Transformer for Boundary-aware Segmentation of CT Images
论文作者
论文摘要
基于计算机断层扫描（CT）的精确前列腺分割用于治疗计划是具有挑战性的，这是由于（1）从CT不良软组织对比的前列腺的不清边界，以及（2）基于卷积神经网络模型捕获长期远程全球环境的限制。在这里，我们提出了一种新型的基于焦点变压器的图像分割体系结构，以有效，有效地从CT图像中提取本地视觉特征和全局上下文。此外，我们设计了一个辅助边界引起的标签回归任务，再加上主要的前列腺分割任务，以解决CT图像中不清楚的边界问题。我们证明，这种设计可显着提高基于CT的前列腺分割任务的质量，而不是其他竞争方法，从而大大提高了性能，即，在私人和公共CT图像数据集上，较高的骰子相似性系数，较低的Hausdorff距离，较低的Hausdorff距离和平均对称的表面距离。我们的代码可在此\ href {https://github.com/chengyinlee/focalunetr.git} {link}中获得。
Computed Tomography (CT) based precise prostate segmentation for treatment planning is challenging due to (1) the unclear boundary of the prostate derived from CT's poor soft tissue contrast and (2) the limitation of convolutional neural network-based models in capturing long-range global context. Here we propose a novel focal transformer-based image segmentation architecture to effectively and efficiently extract local visual features and global context from CT images. Additionally, we design an auxiliary boundary-induced label regression task coupled with the main prostate segmentation task to address the unclear boundary issue in CT images. We demonstrate that this design significantly improves the quality of the CT-based prostate segmentation task over other competing methods, resulting in substantially improved performance, i.e., higher Dice Similarity Coefficient, lower Hausdorff Distance, and Average Symmetric Surface Distance, on both private and public CT image datasets. Our code is available at this \href{https://github.com/ChengyinLee/FocalUNETR.git}{link}.
