
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

72123
10.1038/s41598-024-72123-6
Article
Automatic 3D pelvimetry framework in CT images and its validation
Shao Junlin 1
Wu Qian 2
Zhang Yuqian 1
Liu Changqi 3
Huo Xing 4
Wang Changqing wangchangqing@ahmu.edu.cn

1
1 https://ror.org/03xb04968 grid.186775.a 0000 0000 9490 772X School of Biomedical Engineering, Anhui Medical University, Hefei, 230032 China
2 https://ror.org/03xb04968 grid.186775.a 0000 0000 9490 772X School of Humanistic Medicine, Anhui Medical University, Hefei, 230032 China
3 grid.518584.5 0000 0004 6063 4320 NR Electric Co., Ltd, Nanjing, 211102 China
4 https://ror.org/02czkny70 grid.256896.6 0000 0001 0395 8562 School of Mathematics, Hefei University of Technology, Hefei, 230009 China
13 9 2024
13 9 2024
2024
14 2143114 5 2024
4 9 2024
© The Author(s) 2024
2024
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In the field of spinal pathology, sagittal balance of the spine is usually judged by the spatial structure and morphology of pelvis, which can be represented by pelvic parameters. Pelvic parameters, including pelvic incidence, pelvic tilt and sacral slope, are therefore essential for the diagnosis and treatment of spinal disorders, however, it is a time-consuming and laborious procedure to measure these parameters by traditional methods. In this paper, an automatic measurement framework for pelvic CT images was proposed to calculate three-dimensional (3D) pelvic parameters with the support of deep learning technology. Pelvic images were first preprocessed, and 3D reconstruction was then performed to obtain 3D pelvic model by the Visualization Toolkit. DRINet was trained to segment the femoral head region in the pelvic images, and 3D sphere fitting was performed to locate the femoral heads. In addition, VGG16 was adopted to recognize images containing superior sacral endplate, and the plane growth algorithm was used to fit the plane so that the midpoint and normal vector of the superior sacral endplate could be obtained. Finally, 3D pelvic parameters were automatically calculated, and compared with manual measurements for 15 patients. The proposed framework automatically generated 3D pelvic models, and calculated two-dimensional (2D) and 3D pelvic parameters from continuous CT images. Experiments demonstrated that the framework can greatly speed up the calculation of pelvic parameters, and these parameters are accurate when compared with the manual measurements. In conclusion, the proposed framework demonstrates good performance on automatic pelvimetry measurement by incorporating deep learning technology, and can well replace the traditional methods for pelvic parameter measurement.

Keywords

3D pelvimetry measurement
Pelvic parameters
CT images
3D reconstruction
Deep learning
Subject terms

Image processing
Musculoskeletal system
National Natural Science Foundation of China61872407 Huo Xing issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Sagittal alignment is commonly used to assess the curvature, posture and balance of the spine, as well as their associations with spinal disorders1,2. As an effective tool for the spinal sagittal alignment analysis, pelvic parameters including pelvic incidence (PI), pelvic tilt (PT) and sacral slope (SS), are closely related to the sagittal shape of spine3,4. These pelvic parameters can benefit clinicians to understand situations of the spine and pelvis in the sagittal plane, and then diagnose and develop personalized treatment plans for spinal disorders5,6. In addition, sagittal alignment analysis is of vital importance for the preoperative treatment planning, spinal correction and postoperative recovery of adult spinal deformity4,7. Relevant clinical studies have shown that the pelvic parameters of patients with spondylolisthesis, spinal deformity, and lumbar degenerative disorders have different degrees of characteristic changes4,8,9.

The pelvic parameters, including PI, PT and SS, can be measured by manually determining feature points on sagittal X-Ray images, and connecting these points with hand animation lines. However, it requires much manual work, and two-dimensional (2D) pelvic parameters on the images may be not enough to reflect the real situation of pelvis. With the development of medical image processing technology, many computer-aided measurement software has been recently developed for the sagittal spinopelvic evaluation. Compared with the traditional manual measurements, digital techniques have been proven to be more precise, faster and more reliable10. Korez et al.11 employed a convolutional neural network (CNN) in X-Ray images, and computed 2D spinopelvic parameters. However, this fully automated approach cannot completely divorce from the visual review and confirmation of the measured values by observers. Chen et al.12 proposed a three-dimensional (3D) pelvimetry framework with external auxiliary software, which requires a lot of manual operations and complex interactions, and does not realize automation, so these problems limit its application and promotion. Therefore, a fully automatic pelvimetry framework with a small amount of manual fine-tuning would have a profound impact on the field of pelvic parameter measurement.

To this end, an automatic 3D pelvimetry framework was innovatively proposed to yield 2D/3D pelvic parameters as well as 3D pelvic model, which is conducive for clinicians to quickly understand the real situation of the patients’ pelvises. The proposed framework was aimed to automatically and instantaneously generate the 2D/3D pelvic parameters from CT scans without any manual intervention or complex measurement procedures. It applies the popular deep learning technology, uses a large number of CT scans to train the DRINet13 and VGG1614 networks, and finally obtains the robust trained deep learning networks. Combined with deep learning technology and corresponding traditional algorithms, the stability and adaptability of the framework are greatly enhanced, and the 2D/3D pelvic parameters can be accurately obtained. Considering the convenient usage, the framework was also designed to be user-friendly, and the result of an automatic measurement can also be fine-tuned manually to make it more accurate.

Main difference between the proposed framework and those mentioned above is that the proposed framework uses deep learning technology to recognize the femoral head and the superior sacral endplate automatically, then calculates the corresponding 3D pelvic parameters. The proposed framework is highly promising for usage in clinical applications for the following reasons: (1) extension of 2D pelvic parameters to 3D pelvic parameters can widen the range of clinical spinal research to be more in line with clinical practices; (2) application of deep learning enables the framework to be further upgraded, and the results would become more accurate with the increase of usage; (3) less manual intervention guarantees that the framework is more efficient; (4) combination of manual refinement can make the results more credible.

Materials and methods

The proposed framework consists of four modules: (1) 3D pelvic model reconstruction; (2) detection of spatial femoral head centers; (3) recognition of the superior sacral endplate; (4) calculation of pelvic parameters. The second and third modules can be executed in parallel, and structure of the proposed framework is demonstrated in Fig. 1.Fig. 1 Schematic diagram of the proposed framework. Four modules are included: 3D pelvic model reconstruction (red box), detection of spatial femoral head centers (black box), recognition of the superior sacral endplate (purple box), and calculation of pelvic parameters (blue box).

3D pelvic model reconstruction

Pelvic CT images, including skeleton, soft tissue, organs and other components, are usually acquired from different scanners, thus extraction of bone regions by a classic thresholding method is not accurate enough to obtain a satisfactory pelvic model. Therefore, the K-means algorithm15 was chosen to segment the CT images into background and bone parts. By incorporating the weighted quality evaluation function, this module divided the image pixels into two clusters based on the gray scale feature of CT images. Segmentation quality E was measured by the weighted distance between the pixels and their cluster centers, which can be formulated as follows:1 E=∑i=1KniNσi

where σi represents the standard deviation of the ith cluster, and N is the total number of pixels that need to be segmented,

Firstly, two different gray values were randomly selected as the initial clustering centers on the image to be segmented, and the weighted distance LP,Oi between each pixel and each clustering center was calculated as follows:2 LP,Oi=12N1σidP,Oi2

where d(P,Oi) is the Euclidean distance between object P and object Oi. Then, the pixel was put into the category with the smallest distance, and the clustering centers of each category were updated by using the average value. Next, the cluster quality E was calculated by Eq. (1). If E reached the expected value or the number of iterations reached the maximum number, the process would be stopped; otherwise, the clustering process was re-iterated. Finally, we got the segmented pelvic images. Introduction of these two equations ensures that points at the boundary are more likely to be divided into large clusters, which would make the boundary of image blocks clear and improve the quality of the reconstruction model.

As an open-source software for manipulating and displaying scientific data, the Visualization Toolkit (VTK) comes with state-of-the-art tools for 3D rendering and a suite of widgets for 3D interaction. In addition, VTK is also convenient to use across platforms, thus VTK was chosen as the primary tool of the proposed framework to realize visual operations and data operations. Here the preprocessed CT images were imported into the data pipeline of VTK, and the built-in marching cubes (MC) algorithm was used for 3D reconstruction of pelvic models.

The reconstructed 3D model can be saved into a vtk format file for direct read later, and can also be displayed directly in the system using the visualization pipeline of VTK. An original image, the extracted image of skeleton, and the 3D reconstruction model of pelvis are shown in Fig. 2.Fig. 2 Process of the 3D reconstruction. (a) Original image; (b) Bone part image extracted from the original image; (c) 3D reconstruction model of pelvis.

Detection of spatial femoral head centers

The femoral head is round, and its top half is similar to a sphere. Central points of the two femoral heads are essential reference points in the pelvic parameter measurement. To automatically locate the femoral heads on the established pelvic model, the femoral heads was first segmented on the 2D images, and centers of the femoral heads were then determined and mapped to the 3D model. Core of this module was the segmentation of the femoral heads on 2D images by deep learning technology.

Structure of the DRINet

To recognize the circular regions (i.e., femoral heads) on CT images, the DRINet was used as image segmentation network in this module13. The DRINet is based on the ideas of DenseNet16 and Inception-Resnet17, and consists of three parts: Dense Connection Block (DC_Block), Residual Inception Block (RI_Block) and Unpooling Block (Unpooling_Block). DC_Block is the encoder of the DRINet, and its main function is to extract and aggregate image features. RI_Block and Unpooling_Block are the decoders of the DRINet, and mainly upsample the image and output the final image. RI_Block and Unpooling_Block both adopt multi-branch deconvolution operations, and finally concatenate the features to realize image sampling. BN layers are also used to prevent the gradient from disappearing.

Three DC_Blocks, six RI_Blocks and three Unpooling_Blocks are combined to get the DRINet. The structure of each block and the whole network are shown in Fig. 3.Fig. 3 Structure of the DRINet.

DRINet training

Image data from local hospital and DeepLesion18 were used to train the DRINet, and there were 2500 CT scans containing femoral heads. Pelvic image sequences of 15 patients were locally acquired using a Siemens Definition AS 40 CT system, and scanning parameters are listed as follows: tube voltage, 120 kV; tube current, 154 mA; scanning field of view (DFOV), 407.0 mm; axial slice thickness, 5 mm; inter-slice gap, 5 mm; reconstruction thickness, 1 mm; reconstruction interval, 1 mm; reconstruction matrix, 512 × 512. This study was approved by the Institutional Review Board of Anhui Medical University and in accordance with the Declaration of Helsinki, and informed consent was obtained from all patients.

Before training, the original CT scans were converted into gray images in BMP format to improve training speed. Each image has a size of 512×512, and its corresponding label is an image that contains only the circular area of the femoral head with the same size. In the training stage, the learning rate was set to 0.001 and training epochs to 250, the optimizer was set as Adam, the loss function was set as Dice_loss, and both images and labels were normalized. The image data were divided into a training set (80%) and a validation set (20%), and K-fold cross-validation19 was used in training. Definition of Dice_Loss is formulated as follows:3 Diceloss=1-2X∩YX+Y

where X represents the label image, Y represents the output image, X∩Y represents the intersection of the label and output images, and X and Y represent the number of elements in X and Y respectively.

For an image containing femoral heads, its corresponding output of DRINet should contain circular regions; and for an image in the absence of femoral heads, its corresponding output of DRINet should be blank. Femoral head can be approximated as circle, and the Hough transform algorithm20 was used to detect the centers and radii of the circles in the output images. Figure 4 shows examples of an original image, output image of the DRINet, and image after circle detection.Fig. 4 Examples of an original image, output image of the DRINet, and image after circle detection. (a) Original image; (b) Output image; (c) Circle detection.

3D sphere fitting of femoral head

For images containing femoral heads, center and radius of each femoral head region have been obtained in "DRINet training". For the left and right femoral heads of each patient, centers and radii of the femoral head regions in all CT images were traversed, and therefore the maximum radius and its corresponding center were respectively taken as the radius and hemispherical center of each femoral head.

According to the pixel spacing of the continuous images, the center and radius of each femoral head were mapped into 3D space, and a sphere at the corresponding position was drawn using VTK to fit the femoral head. Because the centers and radii determined on the 2D images are pixel coordinates, we use pixelx to represent the coordinates on the x-axis of the image, xspacing to represent the pixel spacing on the x-axis of the image, and spacex to represent the space coordinate obtained after mapping. We set the serial number of the image which contains a central point of the femoral head as currentz, which represents the position of the central point on the z-axis in the pelvic image sequence. The space point mapping is formulated as follows:4 spacex=pixelx×xspacingspacey=pixely×yspacingspacez=currentz×zspacing

Thereafter, center of the femoral head is denoted as Pf with coordinates of (spacex,spacey,spacez).

Recognition of the superior sacral endplate

For the superior sacral endplate, normal vector Vs and central point Ps are also important variables for calculating pelvic parameters. However, it is time-consuming and laborious to manually determine the position of endplate by conventional methods. In this module, automatic recognition of the superior sacral endplate was carried out by deep neural network, and there were three steps: (1) recognizing a transition image between L5 (i.e., the fifth lumbar vertebra) and S1 (i.e., the first sacral vertebra) by neural network; (2) determining the point set Sp of the superior sacral endplate in the image by analysis of image connected region; (3) mapping the 2D points in Sp into 3D space, then all points and their normal vectors of the endplate were obtained by the plane growth algorithm to calculate Ps and Vs.

Image classification

Recognition of vertebral types in images by neural networks is a classification problem. The VGG16 network14 was empirically chosen as the classification network, and several convolution kernels with size of 3×3 were used to replace the large convolution kernel in the standard CNN. Compared with the standard CNN, the VGG16 not only reduces the number of parameters, but also learns more complex features. However, the standard VGG16 has a large number of neurons in the last fully-connected layer, which may lead to too many parameters and reduce the efficiency of the network, so the number of neurons in the fully-connected layer was reduced appropriately.

A number of 2000 images were selected from DeepLesion (mentioned in "DRINet training") as the training set of the VGG16 network, and the labels were the classification number of each image rather than images. All pelvic slice images were classified into three categories: lumbar vertebrae (ID: 0), S1 (ID: 1) and femoral heads (ID: 2). Because of the particularity of pelvic spatial structure, an image may contain two objectives, so we stipulated that if there were both vertebral L5 and S1 in the image, its category was lumbar vertebrae; if there were both S1 and femoral heads in the image, its category was S1. Figure 5 shows typical images of these three classification categories.Fig. 5 Typical images of the three classification categories. (a) An image with L5 (ID: 0); (b) An image with Sl (ID: 1); (c) An image with femoral heads (ID: 2).

In the training stage, we set the number of neurons in the last three fully-connected layers of the VGG16 network as 128, 256 and 3 respectively. The activation function in the network was the Relu function, the learning rate was 0.001, the optimizer was Adam, the loss function was MSE, and the training epochs was 50. Finally, the training accuracy of the VGG16 network was over 95%, and the network converged with its parameters saved in an h5 file. Output of the network was a vector of 1×3, and each component in the vector represented the probability of classification. Subscript of the component with the highest probability was chosen as the final classification result.

Image connected region analysis

Superior sacral endplate locates below the lumbar L5, and it is a slope, so it spans several 2D pelvic slices. But there is a rule: there must be some points belonging to this plane in several images of the junction of L5 and S1. We chose the images belonging to the junction of L5 and S1 according to the classification results of the VGG16 (generally select one to two images before the image classified as S1), observed them and found that the largest areas near the centerline of these images belong to S1, and the points on the edge of the top part of S1 was on the superior sacral endplate. Therefore, as long as some points of the superior sacral endplate in the 2D image were mapped to the 3D space as anchors, plane-growth can be carried out in the 3D model, and center points and normal vectors of the plane can be obtained.

As for how to get these points, our initial idea was to use deep learning to get the results directly, but the number of images belonging to the junction of L5 and S1 in the dataset was too small to allow the network to be fully trained. So we put forward another method according to the observation: we first used the 4-connected region algorithm to find the largest connected region Rl in an image of the junction of L5 and S1, then we calculated the central point OR of Rl, and took out 20 adjacent points which locates above OR and at the edge of Rl. Most of these 20 points were points on the superior sacral endplate.

Finally, the points on these images were mapped into 3D space by Eq. (4), and these points would be saved in point set Sa to be used in the next step. Figure 6 shows the process of finding points of the top endplate of the S1 on a 2D image.Fig. 6 Process of finding points on the superior sacral endplate in a 2D image. (a) The image at the junction between L5 and S1; (b) The largest connected area; (c) The points on the upper plane of the sacrum.

Plane growth on the 3D model

The point set Sa was obtained in the previous step, and in this step, Sa would be used to find the spatial points on the superior sacral endplate in the 3D pelvic model. The plane growth algorithm is described in Algorithm 1.

Calculation of pelvic parameters

Based on the center point Pf and the radius of femoral head ("Detection of spatial femoral head centers"), and the center point Ps and the normal vector Vs of superior sacral endplate ("Recognition of the superior sacral endplate"), pelvic parameters were calculated. It should be noted that there were two central points of the femoral heads, which were denoted as Pf1 and Pf2 respectively.

Here we specified that the normal unit vector of the horizontal plane of space was Z, then the formulas for calculating the three 3D pelvic parameters were as follows:5 Pmid=Pf1+Pf2/2

6 PI3D=arccos(Ps-Pmid)·Vs‖Ps-Pmid‖‖Vs‖

7 PT3D=arccos(Ps-Pmid)·Z‖Ps-Pmid‖

8 SS3D=arcsinVs·Z‖Vs‖

The calculation of the 3D pelvic parameters is an extension of the 2D pelvic parameters12.

Specifically, when automated measurements fail to meet requirements, the system offers a manual adjustment module. The manual adjustment can utilize the functionality provided by VTK to manually move the predicted femoral head sphere to the actual femoral head position. The manual adjustment module operates on the premise of automated measurements, meaning that before using the manual adjustment module, the corresponding automated measurement module must be called. When using the femoral head center position adjustment module, the 3D sphere representing the femoral head already drawn in the 3D scene was first cleared. Then, the femoral head coordinates obtained from the automated measurement module were modified according to the direction of movement. Finally, based on the new femoral head coordinates, new femoral head was drawn in the 3D scene.

To verify the accuracy of the pelvic parameters automatically generated by the proposed framework, pelvic image sequences of 15 patients (mentioned in "DRINet training") were processed by an orthopedic surgeon. Firstly, the 3D pelvic slice images were mapped to the sagittal plane, and a clinician was asked to measure the pelvic parameters manually on the 2D image. The clinician was then asked to identify the superior sacral endplate and the femoral head in a completely manual manner in the initial framework and then obtained the 3D pelvic parameters. Next, these images were imported into the proposed framework, and the 2D and 3D pelvic parameters were calculated automatically and compared to verify the validity of the pelvic parameters automatically generated by the proposed framework.

Results

Overall effect of the framework

For the obtained 2D central points and radii of femoral heads, the points were mapped to 3D space, and 3D radii were calculated through multiplying the 2D radii by xspacing. Next, the VTK was used to draw the femoral heads on both sides, so that users could intuitively observe the femoral heads. In addition, an artificial fine-tuning function was added in the framework, and users can choose either femoral head to adjust the position to obtain more accurate results. The results of each step are shown in Fig. 7.Fig. 7 Calculation of pelvic parameters. (a,b) Automated recognition of femoral head spheres (shown as red and green spheres); (c) Automated recognition of the superior sacral endplate (red band); (d) Connection of the feature points to draw lines and for calculation of pelvic parameters.

Verification of the accuracy of the DRINet

To test the performance of the DRINet in 2D femoral head segmentation, another three neural networks, including U-Net22, Res-U-Net23 and Dense-U-Net24, were used for comparison in terms of number of parameters, displacement of the central points of the femoral heads, radius error of the femoral heads, networks’ recognition accuracy and the Dice coefficient. The closer to 1 the network recognition accuracy and the Dice coefficient are, the better performance the networks represent, and the smaller central deviation and radius error of the femoral heads are. It should be noted that apart from the network structure, the other three networks were set up in accordance with the DRINet. Table 1 demonstrates that the DRINet is superior to the other three networks in all aspects. Based on the detected circles from the networks’ output images, performance of the segmentation networks is demonstrated in Fig. 8. It can be seen that the circles identified by the DRINet were more in line with the outlines of the femoral heads. However, the convergence speed and training speed of the DRINet were slightly slower than those of the other three networks due to too many branches. Table 1 Verification of the accuracy of 2D femoral head segmentation.

Networks	Parameters	Central deviation of the femoral heads (units: pixels)	Radius error of the femoral heads (units: pixels)	Network recognition accuracy	Dice	
U-Net22	31.03 M	6.410	5.540	0.910	0.90 ± 0.03	
Res-U-Net23	8.23 M	15.830	10.180	0.738	0.82 ± 0.13	
Dense-U-Net24	25.67 M	10.059	5.446	0.923	0.92 ± 0.10	
DRINet13	0.29 M	3.230	1.770	0.961	0.96 ± 0.05	

Fig. 8 Performance of circular recognition in output images of the segmentation networks. (a) Segmentation results of U-Net; (b) Segmentation results of Res-U-Net; (c) Segmentation results of Dense-U-Net; (d) Segmentation results of DRINet.

Comparison of classification accuracy between the VGG16 and other networks

Several classification networks, including the standard CNN25, ResNet5017, DenseNet16 and VGG1614, were selected to class the pelvic images into three categories (lumbar vertebrae, S1 and femoral heads), and compared in terms of number of parameters and accuracy. It should be noted that all networks used for comparison have the same configuration as the VGG16, and MSE was used as the loss function for each network. Table 2 demonstrates that the VGG16 outperforms the standard CNN, ResNet50 and DenseNet in accuracy, but is inferior to the standard CNN in network scale. Considering the accuracy of image classification, the VGG16 network was chosen as the final classification network. Table 2 Comparison of classification accuracy of several networks.

Networks	Parameters	Accuracy	
Standard CNN25	16.79 M	0.984	
ResNet5017	40.39 M	0.911	
DenseNet16	34.08 M	0.985	
VGG1614	31.53 M	0.991	

Comparison between automatically generated and manually measured parameters

Figure 9a,b show a pelvic slice observed from the axis plane and a pelvic image mapped from multiple pelvic slices observed from the sagittal plane. To ensure fairness, the 2D parameters of the pelvis were measured by the clinician on the image mapped to the sagittal plane. Figure 9c shows the original fully manual pelvic measurement framework. The superior sacral endplate is first identified, and then two small spheres are placed in the region of the femoral heads. By adjusting positions of the spheres, the spheres can represent the position of the femoral head, and the measured parameters would be automatically displayed. Here the clinician is asked to manually identify the plane and adjust the position of the two spheres, and get the manual 3D measurement results.Fig. 9 Manual measurement of 2D/3D pelvic parameters. (a) An image on axis plane; (b) The image of the pelvis mapped to the sagittal plane; (c) Manual measurement of pelvic parameters.

After measuring the pelvic images of 15 patients, the pelvic parameters automatically identified and generated by this framework were compared with the two kinds of pelvic parameters described above. The deviation (unit in degree) between the automatically generated results and the other two manual results was recorded in Table 3. Table 3 The average errors between the automatically generated results and the other two results (units in degrees).

Automatic and manual	PI	PT	SS	
3D	2.5 ± 0.5	1.9 ± 0.3	1.2 ± 0.3	
2D	3.0 ± 0.4	2.3 ± 0.5	1.5 ± 0.4	

Discussion

In this work, we have performed extensive experiments to verify the proposed framework and the accuracy of the generated pelvic parameters. The results demonstrate that the proposed framework can identify the corresponding parts of pelvis accurately, and there is just slight difference between the automatically calculated pelvic parameters and the manually measured parameters. Therefore, the automatic pelvimetry framework proposed in this study is promising to replace the traditional measurement methods.

Since goal of this study is to provide the fully automated pelvic parameter measurement framework, thus experiments are mainly used to prove the effectiveness of this framework. In addition, the framework also provides functions, such as manual fitting of the superior sacral endplate, identification of femoral head, and fine-tuning of the identified planes and spheres, as a response to inaccurate or invalid automatic identification. Therefore, the proposed framework has strong adaptability, and can be used as an effective medical assistance tool.

Several previous works have been presented for the pelvic parameter measurement. Computerized software Keops®10 and Surgimap26 are software for parameter measurement by marking landmarks manually on 2D images, and demonstrate accurate results and convenient usage relative to manual measurement. As an improvement, our framework can generate a 3D pelvis model from CT images and automatically measure the pelvic parameters, so that users can observe the pelvis intuitively and the obtained 3D pelvic parameters can accurately reflect the actual situation of the pelvis and spine.

Previous work by Korez et al.11 proposes an automatic method for identification of specific areas and calculation of pelvic parameters from sagittal X-ray images. However, this method can only deal with 2D images, and has not been extended to 3D space. Compared with their measurement methods, the proposed framework for automatically measuring pelvic parameters is obviously convenient and reduces manual operations.

This study has several limitations. First, in the 2D femoral head segmentation module, generalization ability of the DRINet can be further improved by incorporating diverse CT images from different CT scanners and protocols. Second, recognition efficiency of the VGG16 network needs to be further improved for classification of pelvic images under the premise of ensuring accuracy. Third, exception and error handling mechanisms should be considered to enhance robustness of the proposed framework in clinical practice, and further validation of the spinal alignment based on the proposed framework is also warranted in a future study.

Conclusion

In conclusion, a pelvimetry framework is proposed by incorporating image processing techniques and deep learning networks, and automatic pelvic parameter measurement is achieved for pelvic sequence images. The framework demonstrates superior performance to the traditional methods, and can replace manual measurement for accurate pelvic parameters.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (61872407 and 61572167).

Author contributions

X.H. and C.W. designed the experiments, J.S., Q.W., Y.Z. and C.L. conducted the experiments and analyzed the results, C.L. and C.W. wrote the manuscript, and all authors reviewed the manuscript.

Data availability

The datasets prepared for the current study are not publicly available since they are under license permitted only within the current study, but they could be available from the corresponding author upon reasonable request.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Junlin Shao and Qian Wu.
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