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SSDL—an automated semi-supervised deep learning approach for patient-specific 3D reconstruction of proximal femur from QCT images

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Abstract

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.

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Data availability

The datasets presented during the current study are not publicly available due to privacy and ethical restrictions but might be available on reasonable request from the corresponding author.

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Acknowledgements

The authors greatly acknowledge Sarah Doll of Mechanical Engineering at UL Lafayette, USA, and Mashiyat Nayeem of Computer Science of North South University, Bangladesh, for helping on data annotation. The authors acknowledge the intellectual contribution of Rabina Awal of Musculoskeletal Mechanics & Multiscale Materials (4M) Lab in the Mechanical Engineering at UL Lafayette throughout the project. The authors also acknowledge the feedback provided by Dr. Ahmed Suparno Bahar Moni, an Assistant Professor and Orthopedic Surgeon in the Dept. of Orthopaedics at the University of Toledo in assessing our annotation.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh

    Jamalia Sultana & Mahmuda Naznin

  2. Department of Mechanical Engineering, University of Louisiana at Lafayette, Lafayette, LA, 70503, USA

    Tanvir R. Faisal

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  1. Jamalia Sultana
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Contributions

TF and MN conceived the idea, and TF, MN, and JS designed the study. JS developed the model and conducted experiments. JS, MN, and TF conducted the data analysis and interpretation of data. All authors discussed the results and contributed to the drafting of this manuscript. All authors reviewed and approved the final manuscript.

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Correspondence to Tanvir R. Faisal.

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Sultana, J., Naznin, M. & Faisal, T.R. SSDL—an automated semi-supervised deep learning approach for patient-specific 3D reconstruction of proximal femur from QCT images. Med Biol Eng Comput 62, 1409–1425 (2024). https://doi.org/10.1007/s11517-023-03013-8

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