Background and objective

Bone tumor is a kind of harmful orthopedic disease, there are benign and malignant points. Aiming at the problem that the accuracy of the existing machine learning algorithm for bone tumor image segmentation is not high, a bone tumor image segmentation algorithm based on improved full convolutional neural network which consists fully convolutional neural network (FCNN-4s) and conditional random field (CRF).

Methodology

The improved fully convolutional neural network (FCNN-4s) was used to perform coarse segmentation on preprocessed images. Batch normalization layers were added after each convolutional layer to accelerate the convergence speed of network training and improve the accuracy of the trained model. Then, a fully connected conditional random field (CRF) was fused to refine the bone tumor boundary in the coarse segmentation results, achieving the fine segmentation effect.

Results

The experimental results show that compared with the traditional convolutional neural network bone tumor image segmentation algorithm, the algorithm has a great improvement in segmentation accuracy and stability, the average Dice can reach 91.56%, the real-time performance is better.

Conclusion

Compared with the traditional convolutional neural network segmentation algorithm, the algorithm in this paper has a more refined structure, which can effectively solve the problem of over-segmentation and under-segmentation of bone tumors. The segmentation prediction has better real-time performance, strong stability, and can achieve higher segmentation accuracy.

中文翻译：

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基于FCNN-4s和CRF细分割融合算法的骨肿瘤检查

背景和目标

骨肿瘤是一种有害的骨科疾病，有良性和恶性之分。针对现有机器学习算法骨肿瘤图像分割精度不高的问题，提出一种基于改进全卷积神经网络的骨肿瘤图像分割算法，该算法由全卷积神经网络（FCNN-4s）和条件随机场组成（CRF）。

方法

改进的全卷积神经网络（FCNN-4s）用于对预处理图像进行粗分割。在每个卷积层之后添加批量归一化层，以加快网络训练的收敛速度，提高训练模型的准确性。然后融合全连接条件随机场（CRF）来细化粗分割结果中的骨肿瘤边界，达到细分割效果。

结果

实验结果表明，与传统的卷积神经网络骨肿瘤图像分割算法相比，该算法在分割精度和稳定性方面都有很大的提高，平均Dice可以达到91.56%，实时性更好。

结论

与传统的卷积神经网络分割算法相比，本文算法结构更加精细，可以有效解决骨肿瘤的过分割和欠分割问题。分割预测实时性较好，稳定性强，可以达到较高的分割精度。