SCIENTIFIC NEWS AND
INNOVATION FROM ÉTS
Artificial Intelligence Assisting Orthopaedic Imaging - By : Roseline Olory Agomma, Carlos Vázquez, Thierry Cresson, Jacques De Guise,

Artificial Intelligence Assisting Orthopaedic Imaging


Roseline Olory Agomma
Roseline Olory Agomma Author profile
Roseline Olory Agomma is a PhD student in the Department of Software and IT Engineering at ÉTS. She is conducting her studies at the Imaging and Orthopedics Research Laboratory (LIO)

Carlos Vázquez
Carlos Vázquez Author profile
Carlos Vázquez is a professor in the Department of Software and IT Engineering at ÉTS. His research interests include image and video digital processing, stereoscopic and multiview imaging, 3D-TV systems and multiview video coding.

Thierry Cresson
Thierry Cresson Author profile
Thierry Cresson is a research associate at the ÉTS LIO.

Jacques De Guise
Jacques De Guise Author profile
Jacques de Guise is a full professor at the Department of automated manufacturing engineering at the ÉTS and associate professor at the Department of surgery of the Université de Montréal faculty of medicine.

X-ray views of lower limbs

Provided by the authors. CC Licence

SUMMARY

Correctly identifying bone structures on X-rays is an important step in some orthopaedic procedures. This seemingly simple task is executed mainly by radiologists before more complex procedures are performed. Automatic identification of bones in X-rays—especially when the field of view and patient orientation are not initially known and when structures overlap—is a difficult task. The aim of this work is the automatic identification of lower limb bones in X-rays with different fields of view and two patient orientations. The proposed method uses data augmentation to improve training of a deep learning method (SegNet) making the identification of lower limb bones possible. From a validation database of 60 X-rays, we obtained a Dice coefficient of 93.85 +/- 0.02%, proving the relevance of the proposed method. Keywords: Bone structure identification, semantic segmentation, X-ray

A More Accurate Method of Bone Identification

Many orthopaedic procedures involving lower limb bones require X-ray imaging of the patient in a standing position to correctly estimate clinical parameters (e.g. neck-shaft angle, femoral-tibial angle) important in the decision-making process. These clinical parameters are extracted by identifying bones in images [1]. Automated methods for the implementation of this task [2] [3] are described in the literature, but most were tested on X-rays with the same field of view and patient orientation, or were dedicated to identifying a single structure [2] or relatively uncomplicated images (e.g. antero-posterior image analysis) [2]. 

In this article, we propose an identification method for bone structures in X-rays with different fields of view (lower limb images, full-body images and images where the target bone is partially visible) and two patient orientations (0°/90° and 45°/45°), requiring no human intervention. The implemented solution was applied to X-rays acquired through EOS [4] and allowed a precise and robust extraction of four lower limb bones (femur and tibia) despite significant challenges with overlapping bone and muscle structures.

Leg X-rays in different orientations

Figure 1 (a) 0°/90° orientation X-rays (b) 45°/45° orientation X-rays

Proposed Data Augmentation Method

To automatically identify the four bones, we first generated ground truth masks by identifying the contours of each bone structure on the related X-rays (180 in total). From these contours, we created different coloured masks for each structure. In order to improve the training performance of the neural network (SegNet [5]), a data augmentation strategy was proposed to obtain a training database of 43,960 images. The data augmentation strategy was based on 3 transformations: scaling (uniform and non-uniform), cropping, and sliding windows. In the learning phase, the network started with an image and the related mask to learn the characteristics of the target structure in the image, allowing it to estimate the mask. In the test phase, the network started with an image and then generated an output mask, related to the target bone structure, based on the characteristics discovered during the learning phase.

Diagram of SegNet deep learning method

Figure 2 Architecture of the SegNet method

Results of the SegNet Method

For the evaluation phase, we used a database of 60 X-rays acquired through the EOS system. The Dice coefficient served as an evaluation metric to measure the similarity between the ground truth mask and the predicted mask. We obtained a Dice coefficient of 93.85 +/- 0.02%. The figure above shows some of the obtained qualitative results.

Bone identification using deep learning

Figure 3 Comparisons between ground truth masks and predicted masks using the SegNet method

Conclusion

In this work, four lower limb bones were automatically identified on X-rays with varying fields of view and different patient orientations. This solution will allow clinicians to focus on much more useful tasks in the personalized treatment of patients. 

Additional information

For more information on this research, please refer to the following lecture article: 

Olory Agomma, R., Vázquez, C., Cresson, T. and de Guise, J., 2019. “Detection and Identification of Lower-Limb Bones in biplanar X-Ray Images with Arbitrary Field of View and Various Patient Orientations.” In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

Roseline Olory Agomma

Author's profile

Roseline Olory Agomma is a PhD student in the Department of Software and IT Engineering at ÉTS. She is conducting her studies at the Imaging and Orthopedics Research Laboratory (LIO)

Program : Software Engineering 

Research laboratories : LIO – Imaging and orthopedics research laboratory 

Author profile

Carlos Vázquez

Author's profile

Carlos Vázquez is a professor in the Department of Software and IT Engineering at ÉTS. His research interests include image and video digital processing, stereoscopic and multiview imaging, 3D-TV systems and multiview video coding.

Program : Software Engineering  Information Technology Engineering 

Author profile

Thierry Cresson

Author's profile

Thierry Cresson is a research associate at the ÉTS LIO.

Program : Automated Manufacturing Engineering 

Research laboratories : LIO – Imaging and orthopedics research laboratory 

Author profile

Jacques De Guise

Author's profile

Jacques de Guise is a full professor at the Department of automated manufacturing engineering at the ÉTS and associate professor at the Department of surgery of the Université de Montréal faculty of medicine.

Program : Automated Manufacturing Engineering 

Research chair : Canada Research Chair on 3D Imaging and Biomedical Engineering  Marie-Lou and Yves Cotrel Montreal University and ÉTS Research Chair in Orthopaedics 

Research laboratories : LIO – Imaging and orthopedics research laboratory 

Author profile


comments

    Leave a Reply

    Your email address will not be published. Required fields are marked *