提出肿瘤感知的深度学习配准方法，精准保留肺癌患者影像中的肿瘤形态。
Tumor aware recurrent inter-patient deformable image registration of computed tomography scans with lung cancer
- 用3D-LSTM网络分步计算形变场，输入包含肿瘤分割图以增强对肿瘤的保护。
- 在三组数据集上肿瘤体积变化仅0.24%~0.40%，形变后图像强度误差低至0.004。
- 特别适合放射治疗剂量评估，可精确保持肿瘤受照剂量一致性。
背景：基于体素的群体放射治疗（RT）结果建模需要拓扑保持的跨患者可变形图像配准（DIR），既要保留移动图像中的肿瘤，又要避免固定图像中因肿瘤导致的不真实形变。目的：开发一种肿瘤感知的递归配准（TRACER）深度学习方法，并评估其在体素分析中的适用性。方法：TRACER由堆叠的3D卷积长短期记忆网络（3D-CLSTM）编码器、解码器和空间变换层组成，用于计算密集形变向量场（DVF）。通过多步CLSTM逐步生成形变序列。输入条件通过将肿瘤分割图作为额外通道与3D图像对一同输入实现。采用双向肿瘤刚性、图像相似性和形变平滑性损失，在无监督条件下优化网络。在204对肺癌患者3D CT图像上训练，评估使用三组数据集：(a) Dataset I（N=308对，深度学习分割肿瘤）、(b) Dataset II（N=765对，人工勾画肿瘤）、(c) Dataset III（42名接受放疗的肺癌患者）。结果：TRACER能准确配准正常组织。其在肿瘤保持方面表现最优，三组数据集的肿瘤体积差异分别为0.24%、0.40%、0.13%，形变后图像强度均方误差分别为0.005、0.005、0.004。在参考女性和男性患者时，计划放疗剂量差分别仅为0.01 Gy和0.013 Gy。
原文摘要 · Abstract (English)
Background: Voxel-based analysis (VBA) for population level radiotherapy (RT) outcomes modeling requires topology preserving inter-patient deformable image registration (DIR) that preserves tumors on moving images while avoiding unrealistic deformations due to tumors occurring on fixed images. Purpose: We developed a tumor-aware recurrent registration (TRACER) deep learning (DL) method and evaluated its suitability for VBA. Methods: TRACER consists of encoder layers implemented with stacked 3D convolutional long short term memory network (3D-CLSTM) followed by decoder and spatial transform layers to compute dense deformation vector field (DVF). Multiple CLSTM steps are used to compute a progressive sequence of deformations. Input conditioning was applied by including tumor segmentations with 3D image pairs as input channels. Bidirectional tumor rigidity, image similarity, and deformation smoothness losses were used to optimize the network in an unsupervised manner. TRACER and multiple DL methods were trained with 204 3D CT image pairs from patients with lung cancers (LC) and evaluated using (a) Dataset I (N = 308 pairs) with DL segmented LCs, (b) Dataset II (N = 765 pairs) with manually delineated LCs, and (c) Dataset III with 42 LC patients treated with RT. Results: TRACER accurately aligned normal tissues. It best preserved tumors, blackindicated by the smallest tumor volume difference of 0.24\%, 0.40\%, and 0.13 \% and mean square error in CT intensities of 0.005, 0.005, 0.004, computed between original and resampled moving image tumors, for Datasets I, II, and III, respectively. It resulted in the smallest planned RT tumor dose difference computed between original and resampled moving images of 0.01 Gy and 0.013 Gy when using a female and a male reference.
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