Developmental dysplasia of the hip is a medical term representing the hip joint instability that appears mainly in infants. The assessment metric of physician is based on the femoral head coverage rate, which needs to segment the femoral head area in 2D ultrasound images. In this paper, we propose an approach to automatically segment the femoral head. The proposed method consists of two parts, firstly, mean filtering, morphological processing and least squares operation are used to detect the ilium and acetabular bone baseline to coarsely obtain the region of interest of the femoral head, then followed by an improved fully convolutional neural network named FNet which integrates the convolution encoder–decoder architecture, pooling indices and residual connection operation for more accurate segmentation. FNet is trained in a cascaded way, which can help the network learn more features with a limited dataset and thus further improve the segmentation performance. Experimental results show that the proposed method achieved an average dice, recall and IoU value of 0.946, 0.937 and 0.897. Moreover, the features learned by convolutional layers are visualized to demonstrate that FNet can focus on significant features, which is helpful to restore the contour of the femoral head more precisely. In conclusion, the proposed method is capable of segmenting femoral head accurately and guiding the diagnosis of developmental dysplasia of the hip.

中文翻译：

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基于改进全卷积神经网络的超声图像股骨头分割

髋关节发育不良是一个医学术语，代表主要出现在婴儿中的髋关节不稳定。医师的评估指标是基于股骨头覆盖率，需要对二维超声图像中的股骨头区域进行分割。在本文中，我们提出了一种自动分割股骨头的方法。所提出的方法由两部分组成，首先使用均值滤波、形态学处理和最小二乘运算检测髂骨和髋臼骨基线粗略获得股骨头的感兴趣区域，然后是改进的全卷积神经网络名为 FNet，它集成了卷积编码器-解码器架构、池化索引和残差连接操作，以实现更准确的分割。FNet以级联方式训练，这可以帮助网络在有限的数据集上学习更多的特征，从而进一步提高分割性能。实验结果表明，该方法的平均骰子、召回率和 IoU 值分别为 0.946、0.937 和 0.897。此外，将卷积层学习到的特征可视化，以证明 FNet 可以专注于重要特征，这有助于更精确地恢复股骨头的轮廓。总之，所提出的方法能够准确地分割股骨头并指导髋关节发育不良的诊断。将卷积层学习到的特征进行可视化，以证明 FNet 可以专注于显着特征，这有助于更精确地恢复股骨头的轮廓。总之，所提出的方法能够准确地分割股骨头并指导髋关节发育不良的诊断。将卷积层学习到的特征进行可视化，以证明 FNet 可以专注于显着特征，这有助于更精确地恢复股骨头的轮廓。总之，所提出的方法能够准确地分割股骨头并指导髋关节发育不良的诊断。