Image registration and segmentation are the two most studied problems in medical image analysis. Deep learning algorithms have recently gained a lot of attention due to their success and state-of-the-art results in variety of problems and communities. In this paper, we propose a novel, efficient, and multi-task algorithm that addresses the problems of image registration and brain tumor segmentation jointly. Our method exploits the dependencies between these tasks through a natural coupling of their interdependencies during inference. In particular, the similarity constraints are relaxed within the tumor regions using an efficient and relatively simple formulation. We evaluated the performance of our formulation both quantitatively and qualitatively for registration and segmentation problems on two publicly available datasets (BraTS 2018 and OASIS 3), reporting competitive results with other recent state-of-the-art methods. Moreover, our proposed framework reports significant amelioration (p < 0.005) for the registration performance inside the tumor locations, providing a generic method that does not need any predefined conditions (e.g., absence of abnormalities) about the volumes to be registered. Our implementation is publicly available online at https://github.com/TheoEst/joint_registration_tumor_segmentation.

中文翻译：

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基于深度学习的并发大脑配准和肿瘤分割


图像配准和分割是医学图像分析中研究最多的两个问题。深度学习算法最近因其在各种问题和社区中的成功和最先进的结果而受到了广泛关注。在本文中，我们提出了一种新颖、高效的多任务算法，共同解决图像配准和脑肿瘤分割问题。我们的方法通过推理过程中相互依赖性的自然耦合来利用这些任务之间的依赖性。特别是，使用有效且相对简单的公式在肿瘤区域内放松了相似性约束。我们在两个公开数据集（BraTS 2018 和 OASIS 3）上定量和定性地评估了我们的配方在配准和分割问题上的性能，报告了与其他最新最先进方法的竞争结果。此外，我们提出的框架报告了肿瘤位置内的配准性能的显着改善（p < 0.005），提供了一种不需要关于要配准的体积的任何预定义条件（例如，不存在异常）的通用方法。我们的实现可在线公开获取：https://github.com/TheoEst/joint_registration_tumor_segmentation。