Purpose

We investigated the parameter configuration in the automatic liver and tumor segmentation using a convolutional neural network based on 2.5D model. The implementation of 2.5D model shows promising results since it allows the network to have a deeper and wider network architecture while still accommodates the 3D information. However, there has been no detailed investigation of the parameter configurations on this type of network model.

Methods

Some parameters, such as the number of stacked layers, image contrast, and the number of network layers, were studied and implemented on neural networks based on 2.5D model. Networks are trained and tested by utilizing the dataset from liver and tumor segmentation challenge (LiTS). The network performance was further evaluated by comparing the network segmentation with manual segmentation from nine technical physicians and an experienced radiologist.

Results

Slice arrangement testing shows that multiple stacked layers have better performance than a single-layer network. However, the dice scores start decreasing when the number of stacked layers is more than three layers. Adding higher number of layers would cause overfitting on the training set. In contrast enhancement test, implementing contrast enhancement method did not show a statistically significant different to the network performance. While in the network layer test, adding more layers to the network architecture does not always correspond to the increasing dice score result of the network.

Conclusions

This paper compares the performance of the network based on 2.5D model using different parameter configurations. The result obtained shows the effect of each parameter and allow the selection of the best configuration in order to improve the network performance in the application of automatic liver and tumor segmentation.

中文翻译：

---

基于2.5D模型的卷积神经网络实现可靠的肝脏和肿瘤自动分割

目的

我们使用基于2.5D模型的卷积神经网络研究了自动肝脏和肿瘤分割中的参数配置。2.5D模型的实现显示出令人鼓舞的结果，因为它允许网络拥有更广泛的网络架构，同时仍可容纳3D信息。但是，尚未对此类型的网络模型上的参数配置进行详细调查。

方法

在基于2.5D模型的神经网络上研究并实现了一些参数，如堆叠层数，图像对比度和网络层数。利用肝脏和肿瘤分割挑战（LiTS）的数据集对网络进行训练和测试。通过比较网络分割与来自九位技术医生和经验丰富的放射科医生的手动分割，进一步评估了网络性能。

结果

切片排列测试表明，多层堆栈比单层网络具有更好的性能。但是，当堆叠的层数超过三层时，骰子分数开始降低。添加更多的层数会导致训练集过度拟合。在对比增强测试中，实施对比增强方法并没有显示出与网络性能有统计学差异。在进行网络层测试时，向网络体系结构添加更多层并不总是对应于网络增加的骰子得分结果。

结论

本文比较了使用不同参数配置的基于2.5D模型的网络性能。获得的结果显示了每个参数的效果，并允许选择最佳配置，以便在自动肝和肿瘤分割的应用中改善网络性能。