论文标题
解析挑战2022：使用SWIN U-NET变压器（SWIN UNETR）和U-NET进行肺动脉分割
PARSE challenge 2022: Pulmonary Arteries Segmentation using Swin U-Net Transformer(Swin UNETR) and U-Net
论文作者
论文摘要
在这项工作中，我们提出了我们提出的方法，该方法是使用SWIN UNETR和基于U-NET的深神经网络体系结构从CT扫描中分割肺动脉的方法。六个型号，基于Swin Unet的三个模型以及基于3D U-NET的三个模型，使用加权平均值进行合奏，以制作最终的分割掩码。通过这种方法，我们的团队获得了84.36％的多级骰子得分。我们的工作代码可在以下链接上提供：https：//github.com/akansh12/parse2022。这项工作是Miccai Parse 2022挑战的一部分。
In this work, we present our proposed method to segment the pulmonary arteries from the CT scans using Swin UNETR and U-Net-based deep neural network architecture. Six models, three models based on Swin UNETR, and three models based on 3D U-net with residual units were ensemble using a weighted average to make the final segmentation masks. Our team achieved a multi-level dice score of 84.36 percent through this method. The code of our work is available on the following link: https://github.com/akansh12/parse2022. This work is part of the MICCAI PARSE 2022 challenge.
