Tan Man, Wu Fa, Kong Dexing, Mao Xiongwei
The School of Mathematical Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
The Radiology Department, The Hospital of Zhejiang University, Hangzhou, Zhejiang, 310058, China.
Med Phys. 2021 Apr;48(4):1707-1719. doi: 10.1002/mp.14732. Epub 2021 Mar 4.
Automatic liver segmentation from abdominal computed tomography (CT) images is a fundamental task in computer-assisted liver surgery programs. Many liver segmentation algorithms are very sensitive to fuzzy boundaries and heterogeneous pathologies, especially when the data are scarce. To solve these problems, we propose an automatic liver segmentation framework based on three-dimensional (3D) convolutional neural networks with a hybrid loss function.
Two networks are incorporated in our method with the first being a liver shape autoencoder that is trained to obtain compressed codes of liver shapes, and the second being a liver segmentation network that is trained with a hybrid loss function. The design of the hybrid loss function is comprised of three parts. The first part is an adaptively weighted cross-entropy loss, which pays more attention to misclassified pixels. The second part is an edge-preserving smoothness loss, which guarantees that the adjacent pixels with the same label have similar outputs, while dissimilar for pixels with different labels. The third part of the loss is a shape constraint to model high-level structural differences based on the learned shape codes. Both networks use 3D operations for data processing. In our experiments, data augmentation is performed at both the training and the test stage.
We extensively evaluated our method on two datasets: the Segmentation of the Liver Competition 2007 (Sliver07), and the Combined (CT-MR) Healthy Abdominal Organ Segmentation (CHAOS) Challenge. Finally, with only 20 training scans, we achieved the best score of 82.55 on the Sliver07 challenge, and a score of 83.02 on the CHAOS challenge.
In this study, we proposed a novel hybrid loss to overcome the difficulties in liver segmentation. The quantitative and qualitative results demonstrate that our method is highly suited for pathological liver segmentation, even when trained with a small dataset.
从腹部计算机断层扫描（CT）图像中自动分割肝脏是计算机辅助肝脏手术程序中的一项基本任务。许多肝脏分割算法对模糊边界和异质性病变非常敏感，尤其是在数据稀缺时。为了解决这些问题，我们提出了一种基于具有混合损失函数的三维（3D）卷积神经网络的自动肝脏分割框架。
我们的方法中包含两个网络，第一个是肝脏形状自动编码器，其经过训练以获得肝脏形状的压缩代码，第二个是使用混合损失函数进行训练的肝脏分割网络。混合损失函数的设计由三部分组成。第一部分是自适应加权交叉熵损失，它更关注误分类的像素。第二部分是边缘保持平滑损失，它保证具有相同标签的相邻像素具有相似的输出，而对于具有不同标签的像素则不同。损失的第三部分是基于学习到的形状代码对高级结构差异进行建模的形状约束。两个网络都使用3D操作进行数据处理。在我们的实验中，在训练和测试阶段都进行了数据增强。
我们在两个数据集上广泛评估了我们的方法：2007年肝脏分割竞赛（Sliver07）和联合（CT-MR）健康腹部器官分割（CHAOS）挑战赛。最后，仅使用20次训练扫描，我们在Sliver07挑战赛上获得了82.55的最佳分数，在CHAOS挑战赛上获得了83.02的分数。
在本研究中，我们提出了一种新颖的混合损失来克服肝脏分割中的困难。定量和定性结果表明，即使在小数据集上进行训练，我们的方法也非常适合病理性肝脏分割。
