For 3D medical image (e.g. CT and MRI) segmentation, the difficulty of segmenting each slice in a clinical case varies greatly. Previous research on volumetric medical image segmentation in a slice-by-slice manner conventionally use the identical 2D deep neural network to segment all the slices of the same case, ignoring the data heterogeneity among image slices. In this paper, we focus on multi-modal 3D MRI brain tumor segmentation and propose a dynamic architecture network named Med-DANet based on adaptive model selection to achieve effective accuracy and efficiency trade-off. For each slice of the input 3D MRI volume, our proposed method learns a slice-specific decision by the Decision Network to dynamically select a suitable model from the predefined Model Bank for the subsequent 2D segmentation task. Extensive experimental results on both BraTS 2019 and 2020 datasets show that our proposed method achieves comparable or better results than previous state-of-the-art methods for 3D MRI brain tumor segmentation with much less model complexity. Compared with the state-of-the-art 3D method TransBTS, the proposed framework improves the model efficiency by up to 3.5x without sacrificing the accuracy. Our code will be publicly available soon.
翻译:对于 3D 医学图象( 如CT 和 MRI ) 分解, 临床病例中将每个切片分解的难度大不相同。 以往关于以切片切片方式进行体积医学图象分解的研究表明, 在临床病例中将每个切片分解的难度大不相同。 以往关于以切片切片方式进行体积医学图谱分割的研究通常使用相同的 2D 深神经网络来分割同一病例的所有切片, 忽略图像切片之间的数据差异性。 在本文中, 我们侧重于多式的 3D MRI 脑肿瘤分解的多式 3D MRI 模式, 并提议一个名为MD- Danet 的动态结构网络, 以适应模式选择有效、 效率权衡。 对于投入 3D MRI 体积的每切片, 我们建议的方法将学会一个切片分化决定, 动态地从预定义的2D 模型库中选择一个合适的模型,, 在随后的 2DRansB 数据集上显示, 我们提议的3D 3D 的模型将很快改进现有公共精确度框架。