MIND & MIND-SSC: Contrast- and Modality-invariant Image Similarity for Multimodal Image Registration

👉 OBELISK 方法通过可变形卷积实现深度学习，从而减少层数来解决 3D 多器官分割问题¹

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👉 Contrast- and modality-invariant image similarity

• 模态独立邻域描述符（MIND） 是一种多维局部图像描述符，可实现多模态配准。在配准单模态的扫描时，它还被证明可以提高准确性和鲁棒性。每个 MIND 描述符只计算在一个 patch 内的距离（一个扫描的局部邻域内）。MIND 的比较是以采样样例的平方/绝对差之和来表示的。

  The Modality independent neighbourhood descriptor (MIND) is a multi-dimensional local image descriptor, which enables multi-modal registration. It has also been shown to improve accuracy and robustness when registering scans of the same modality. Each MIND descriptor is calculated based on patch distances (within the local neighbourhood of the same scan). Comparison of MIND representations is performed as sum of squared/absolute differences of its entries.

• 自相似上下文（SSC）是 MIND 的改进，它重新定义了邻域布局以提高匹配的鲁棒性。它还带有有效的量化方案，允许使用 Hamming weight 计算成对距离。

  The self-similarity context (SSC) is an improvement of MIND, which redefines the neighbourhood layout to improve the robustness of the matching. It also comes with an efficient quantisation scheme, which allows the computation of pair-wise distances using the Hamming weight. Matlab code is available to extract MIND/SSC descriptors for 3D volumes and calculate a distance image. Derivatives can be estimated using finite differences.