论文标题
多模式性腹部多器官分割，深度监督3D分割模型
Multi-Modality Abdominal Multi-Organ Segmentation with Deep Supervised 3D Segmentation Model
论文作者
论文摘要
为了促进医学图像分割技术的开发，提供了用于多功能医疗图像分割的大型腹部多器官数据集Amos，并通过使用数据集进行了AMOS 2022挑战。在本报告中，我们介绍了AMOS 2022挑战的解决方案。我们采用具有深度超级视觉的剩余U-NET作为我们的基本模型。实验结果表明，仅CT任务和CT/MRI任务的平均骰子相似性系数和归一化表面骰子的平均得分分别为0.8504和0.8476。
To promote the development of medical image segmentation technology, AMOS, a large-scale abdominal multi-organ dataset for versatile medical image segmentation, is provided and AMOS 2022 challenge is held by using the dataset. In this report, we present our solution for the AMOS 2022 challenge. We employ residual U-Net with deep super vision as our base model. The experimental results show that the mean scores of Dice similarity coefficient and normalized surface dice are 0.8504 and 0.8476 for CT only task and CT/MRI task, respectively.
