Morphology-guided deep learning framework for segmentation of pancreas in computed tomography images.
Morphology-guided deep learning framework for segmentation of pancreas in computed tomography images.
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DOI：
10.1117/1.jmi.9.2.024002
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发表时间：
2022-03
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影响因子：
--
通讯作者：
Li D
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作者：
Qureshi TA;Lynch C;Azab L;Xie Y;Gaddam S;Pandol SJ;Li D
使用腹部计算机断层扫描（CT）扫描对胰腺进行准确分割是计算机辅助诊断系统检测病理并对胰腺疾病进行定量评估的先决条件。手动勾画胰腺轮廓是繁琐的，耗时的，并且容易出现主观错误，因此显然不是大型数据集的可行解决方案。我们引入了一个多相形态学引导的深度学习框架，用于对CT图像中的胰腺进行有效的三维分割。该方法的工作原理是使用修改后的视觉几何组-19架构定位胰腺，这是一个19层卷积神经网络模型，有助于减少感兴趣区域，以实现更有效的计算，并在分割过程中去除了大部分外围结构。随后，软标签的胰腺局部区域的分割使用U-网模型生成。最后，该模型结合胰腺的形态学先验知识来更新软标签并进行分割。形态先验是单个三维矩阵，定义在来自多个CT腹部图像的胰腺的一般形状和大小上，其有助于改善胰腺的分割。该系统在美国国立卫生研究院的数据集（82个健康胰腺的CT扫描）上进行了训练和测试。在四重交叉验证中，该系统产生了88.53%的平均Dice-Serrensen系数，并且优于最先进的技术。定位胰腺有助于减少分割错误并消除考虑的外围结构。此外，形态学引导的模型有效地改善了胰腺的整体分割。
Accurate segmentation of the pancreas using abdominal computed tomography (CT) scans is a prerequisite for a computer-aided diagnosis system to detect pathologies and perform quantitative assessment of pancreatic disorders. Manual outlining of the pancreas is tedious, time-consuming, and prone to subjective errors, and thus clearly not a viable solution for large datasets. We introduce a multiphase morphology-guided deep learning framework for efficient three-dimensional segmentation of the pancreas in CT images. The methodology works by localizing the pancreas using a modified visual geometry group-19 architecture, which is a 19-layer convolutional neural network model that helped reduce the region of interest for more efficient computation and removed most of the peripheral structures from consideration during the segmentation process. Subsequently, soft labels for segmentation of the pancreas in the localized region were generated using the U-net model. Finally, the model integrates the morphology prior of the pancreas to update soft labels and perform segmentation. The morphology prior is a single three-dimensional matrix, defined over the general shape and size of the pancreases from multiple CT abdominal images, that helps improve segmentation of the pancreas. The system was trained and tested on the National Institutes of Health dataset (82 CT scans of the healthy pancreas). In fourfold cross-validation, the system produced an average Dice–SØrensen coefficient of 88.53% and outperformed state-of-the-art techniques. Localizing the pancreas assists in reducing segmentation errors and eliminating peripheral structures from consideration. Additionally, the morphology-guided model efficiently improves the overall segmentation of the pancreas.