Development of automatic measurement for patellar height based on deep learning and knee radiographs

Ye, Qin; Shen, Qiang; Yang, Wei; Huang, Shuai; Jiang, Zhiqiang; He, Linyang; Gong, Xiangyang

doi:10.1007/s00330-020-06856-z

Cited by 20 publications

(17 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some authors have also developed DCNN algorithms to measure other indices on plain films. Ye et al [ 22 ] developed a deep learning-based system for automatic patellar height measurements using knee radiographs, which can predict the Insall–Salvati, Caton–Deschamps, modified Caton–Deschamps, and Keerati indexes automatically with high accuracy. Li et al [ 23 ] used a mask regional CNN model to detect four key points that delineate the Sharp’s angle.…”

Section: Discussionmentioning

confidence: 99%

Fully automated measurement on coronal alignment of lower limbs using deep convolutional neural networks on radiographic images

Meng

Wang

et al. 2022

BMC Musculoskelet Disord

View full text Add to dashboard Cite

Background A deep convolutional neural network (DCNN) system is proposed to measure the lower limb parameters of the mechanical lateral distal femur angle (mLDFA), medial proximal tibial angle (MPTA), lateral distal tibial angle (LDTA), joint line convergence angle (JLCA), and mechanical axis of the lower limbs. Methods Standing X-rays of 1000 patients’ lower limbs were examined for the DCNN and assigned to training, validation, and test sets. A coarse-to-fine network was employed to locate 20 key landmarks on both limbs that first recognised the regions of hip, knee, and ankle, and subsequently outputted the key points in each sub-region from a full-length X-ray. Finally, information from these key landmark locations was used to calculate the above five parameters. Results The DCNN system showed high consistency (intraclass correlation coefficient > 0.91) for all five lower limb parameters. Additionally, the mean absolute error (MAE) and root mean squared error (RMSE) of all angle predictions were lower than 3° for both the left and right limbs. The MAE of the mechanical axis of the lower limbs was 1.124 mm and 1.416 mm and the RMSE was 1.032 mm and 1.321 mm, for the right and left limbs, respectively. The measurement time of the DCNN system was 1.8 ± 1.3 s, which was significantly shorter than that of experienced radiologists (616.8 ± 48.2 s, t = -180.4, P < 0.001). Conclusions The proposed DCNN system can automatically measure mLDFA, MPTA, LDTA, JLCA, and the mechanical axis of the lower limbs, thus helping physicians manage lower limb alignment accurately and efficiently. Supplementary Information The online version contains supplementary material available at 10.1186/s12891-022-05818-4.

show abstract

Section: Discussionmentioning

confidence: 99%

Fully automated measurement on coronal alignment of lower limbs using deep convolutional neural networks on radiographic images

Meng

Wang

et al. 2022

BMC Musculoskelet Disord

View full text Add to dashboard Cite

show abstract

“…Some authors have also developed DCNN algorithms to measure other indices on plain lms. Ye et al [21] developed a deep learning-based system for automatic patellar height measurements using knee radiographs, which can predict the Insall-Salvati index, Caton-Deschamps index, modi ed Caton-Deschamps index, and Keerati index automatically with high accuracy. Li et al [22] used a mask regional CNN model to detect four key points that delineate the Sharp's angle.…”

Section: Discussionmentioning

confidence: 99%

Fully Automated Measurement on Coronal Alignment of Lower Limbs using Deep Convolutional Neural Networks on Radiographic Images

Meng

Wang

et al. 2022

Preprint

View full text Add to dashboard Cite

Background A deep convolutional neural network (DCNN) system is proposed to measure the lower limb parameters of the mechanical lateral distal femur angle (mLDFA), medial proximal tibial angle (MPTA), lateral distal tibial angle (LDTA), joint line convergence angle (JLCA), and mechanical axis of the lower limbs. Methods Standing X-rays of 1000 patients’ lower limbs were examined for the DCNN and assigned to training, validation, and test sets. A coarse-to-fine network was employed to locate 20 key landmarks on both limbs that first recognized the regions of hip, knee, and ankle, and subsequently outputted the key points in each sub-region from a full-length X-ray. Finally, information from these key landmark locations was used to calculate the above five parameters. Results The DCNN system showed high consistency (ICC > 0.91) for all five lower limb parameters. Additionally, the mean absolute error (MAE) and root mean squared error (RMSE) of all angle predictions were lower than 3° for both the left and right limbs. The MAE of the mechanical axis of the lower limbs was 1.124 mm and 1.416 mm and the RMSE was 1.032 mm and 1.321 mm, for the right and left limbs, respectively. The measurement time of the DCNN system was 1.8 ± 1.3 s, which was significantly shorter than that of experienced radiologists (616.8 ± 48.2 s, t = -180.4, P༜0.001). Conclusions The proposed DCNN system can automatically measure mLDFA, MPTA, LDTA, JLCA, and the mechanical axis of the lower limbs, thus helping physicians manage lower limb alignment accurately and efficiently.

show abstract

“…MRI is the recommended imaging modality for assessing OA-related soft tissues, such as cartilage and menisci, to diagnose OA, stage the disease, and monitor treatment responses [ 9 ]. MRI-based quantification of articular cartilage volume, thickness, and minimal joint space width (mJSW) has been widely investigated [ 10 , 11 ]. Quantification of such knee joint morphological signatures requires careful review and manual segmentation of cartilage from MRI sequences, which is time-consuming and subject to inter-observer variability [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Fully Automatic Knee Joint Segmentation and Quantitative Analysis for Osteoarthritis from Magnetic Resonance (MR) Images Using a Deep Learning Model

et al. 2022

View full text Add to dashboard Cite

Background We aimed to develop and evaluate a deep learning-based method for fully automatic segmentation of knee joint MR imaging and quantitative computation of knee osteoarthritis (OA)-related imaging biomarkers. Material/Methods This retrospective study included 843 volumes of proton density-weighted fat suppression MR imaging. A convolutional neural network segmentation method with multiclass gradient harmonized Dice loss was trained and evaluated on 500 and 137 volumes, respectively. To assess potential morphologic biomarkers for OA, the volumes and thickness of cartilage and meniscus, and minimal joint space width (mJSW) were automatically computed and compared between 128 OA and 162 control data. Results The CNN segmentation model produced reasonably high Dice coefficients, ranging from 0.948 to 0.974 for knee bone compartments, 0.717 to 0.809 for cartilage, and 0.846 for both lateral and medial menisci. The OA-related biomarkers computed from automatic knee segmentation achieved strong correlation with those from manual segmentation: average intraclass correlations of 0.916, 0.899, and 0.876 for volume and thickness of cartilage, meniscus, and mJSW, respectively. Volume and thickness measurements of cartilage and mJSW were strongly correlated with knee OA progression. Conclusions We present a fully automatic CNN-based knee segmentation system for fast and accurate evaluation of knee joint images, and OA-related biomarkers such as cartilage thickness and mJSW were reliably computed and visualized in 3D. The results show that the CNN model can serve as an assistant tool for radiologists and orthopedic surgeons in clinical practice and basic research.

show abstract

Development of automatic measurement for patellar height based on deep learning and knee radiographs

Cited by 20 publications

References 26 publications

Fully automated measurement on coronal alignment of lower limbs using deep convolutional neural networks on radiographic images

Fully automated measurement on coronal alignment of lower limbs using deep convolutional neural networks on radiographic images

Fully Automated Measurement on Coronal Alignment of Lower Limbs using Deep Convolutional Neural Networks on Radiographic Images

Fully Automatic Knee Joint Segmentation and Quantitative Analysis for Osteoarthritis from Magnetic Resonance (MR) Images Using a Deep Learning Model

Contact Info

Product

Resources

About