Deep Learning (DL) techniques have recently been used in medical image segmentation and the reconstruction of 3D anatomies of a human body. In this work, we propose a semi-supervised DL (SSDL) approach utilizing a CNN-based 3D U-Net model for femur segmentation from sparsely annotated quantitative computed tomography (QCT) slices. Specifically, QCT slices at the proximal end of the femur forming ball and socket joint with acetabulum were annotated for precise segmentation, where a segmenting binary mask was generated using a 3D U-Net model to segment the femur accurately. A total of 5474 QCT slices were considered for training among which 2316 slices were annotated. 3D femurs were further reconstructed from segmented slices employing polynomial spline interpolation. Both qualitative and quantitative performance of segmentation and 3D reconstruction were satisfactory with more than 90% accuracy achieved for all of the standard performance metrics considered. The spatial overlap index and reproducibility validation metric for segmentation—Dice Similarity Coefficient was 91.8% for unseen patients and 99.2% for validated patients. An average relative error of 12.02% and 10.75% for volume and surface area, respectively, were computed for 3D reconstructed femurs. The proposed approach demonstrates its effectiveness in accurately segmenting and reconstructing 3D femur from QCT slices.

Graphical abstract

中文翻译：

---

SSDL——一种自动半监督深度学习方法，用于根据 QCT 图像对股骨近端进行患者特异性 3D 重建

深度学习 (DL) 技术最近已用于医学图像分割和人体 3D 解剖结构重建。在这项工作中，我们提出了一种半监督深度学习 (SSDL) 方法，利用基于 CNN 的 3D U-Net 模型从稀疏注释的定量计算机断层扫描 (QCT) 切片中进行股骨分割。具体来说，对股骨近端与髋臼形成球窝关节的 QCT 切片进行注释以进行精确分割，其中使用 3D U-Net 模型生成分割二进制掩模以准确分割股骨。总共考虑了 5474 个 QCT 切片进行训练，其中 2316 个切片进行了注释。使用多项式样条插值从分段切片进一步重建 3D 股骨。分割和 3D 重建的定性和定量性能均令人满意，所有考虑的标准性能指标的准确度均达到 90% 以上。分割的空间重叠指数和再现性验证指标 - Dice 相似系数对于未见过的患者为 91.8%，对于已验证的患者为 99.2%。计算出 3D 重建股骨的体积和表面积的平均相对误差分别为 12.02% 和 10.75%。所提出的方法证明了其在从 QCT 切片中准确分割和重建 3D 股骨方面的有效性。

图形概要