基于注意力的CT扫描插值用于结直肠癌肝转移病灶分割
Attention-based CT Scan Interpolation for Lesion Segmentation of Colorectal Liver Metastases
AI总结：
针对CT切片厚度差异导致CNN分割性能下降的问题，提出无监督注意力插值模型生成中间切片，整合分割损失并突出肝脏病灶区域，在CRLM数据集上提升Dice分数并优于其他插值方法。
摘要：
结直肠癌肝转移（CRLMs）中常见的小肝脏病灶对卷积神经网络（CNN）分割模型构成挑战，尤其是在CT扫描切片厚度差异较大的情况下。CT图像的切片厚度可能因临床指征而异。例如，在需要小血管精细解剖细节的术前规划中，会使用更薄的切片。在尽可能降低患者有效辐射剂量的同时，由于CRLMs的限制，采用了不同的切片厚度。然而，CT间切片厚度的差异导致基于CNN的CT分割模型性能显著下降。本文提出了一种新颖的无监督基于注意力的插值模型，用于从CT扫描中连续的三联体切片生成中间切片。我们在插值模型的训练过程中整合了分割损失，以利用现有切片中的分割标签来生成中间切片。与CT体积中常见的插值技术不同，我们的模型在插值切片中突出显示腹部CT扫描中的感兴趣区域（肝脏和病灶）。此外，我们模型的输出与原始输入切片一致，同时提高了两个前沿3D分割管道的分割性能。我们在CRLM数据集上测试了所提出的模型，对厚切片受试者进行上采样，并为我们的分割模型创建各向同性体积。生成的各向同性数据集提高了病灶分割的Dice分数，并在插值指标方面优于其他插值方法。
英文摘要：
Small liver lesions common to colorectal liver metastases (CRLMs) are challenging for convolutional neural network (CNN) segmentation models, especially when we have a wide range of slice thicknesses in the computed tomography (CT) scans. Slice thickness of CT images may vary by clinical indication. For example, thinner slices are used for presurgical planning when fine anatomic details of small vessels are required. While keeping the effective radiation dose in patients as low as possible, various slice thicknesses are employed in CRLMs due to their limitations. However, differences in slice thickness across CTs lead to significant performance degradation in CT segmentation models based on CNNs. This paper proposes a novel unsupervised attention-based interpolation model to generate intermediate slices from consecutive triplet slices in CT scans. We integrate segmentation loss during the interpolation model's training to leverage segmentation labels in existing slices to generate middle ones. Unlike common interpolation techniques in CT volumes, our model highlights the regions of interest (liver and lesions) inside the abdominal CT scans in the interpolated slice. Moreover, our model's outputs are consistent with the original input slices while increasing the segmentation performance in two cutting-edge 3D segmentation pipelines. We tested the proposed model on the CRLM dataset to upsample subjects with thick slices and create isotropic volume for our segmentation model. The produced isotropic dataset increases the Dice score in the segmentation of lesions and outperforms other interpolation approaches in terms of interpolation metrics.
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