Main Logo
Logo

Society for Pediatric Radiology – Poster Archive

  201
  0
  0
 
 


Final ID: Poster #: SCI-022

Automating Cobb Angle Measurement for Adolescence Idiopathic Scoliosis Using Instance Segmentation

Purpose or Case Report: Scoliosis is a deformity of the spine that affects 2-3% of the population. Currently, the reference standard for assessing scoliosis is the manual assignment of Cobb angles at the site of the curvature center using X-ray images. This process is time consuming and unreliable as it is affected by inter- and intra-observer variability. To overcome these inaccuracies, machine learning (ML) methods can be used to automate the Cobb angle measurement process.
Methods & Materials: We propose to address the Cobb angle measurement task using YOLACT, an instance segmentation model. YOLACT is composed of a ResNet backbone that produces a feature pyramid based on the input image. The information inside the feature pyramid is then passed into two parallel pathways. One pathway is the Protonet that performs the segmentation task. The second pathway finds the mask coefficients of the image to produce image masks. Finally, the algorithm linearly combines the two outputs and uses sigmoid nonlinearity to produce the final mask.

After YOLACT finds the segmentation of each vertebra in the X-ray image, using image processing, we extract segmentation contours and detect the minimum bounding box of each segmentation. The four corners of each box are our landmarks. Lastly, we developed an algorithm that iterates through all the extracted landmarks to find the maximum angles.
Results: We trained and tested our model on a cohort of adolescent patients (AASCE-MICCAI 2019 challenge) (n=609) with 80%/20% training and test ratio. Overall, YOLACT produces acceptable segmentations that follow the curve of the spine with no major deviation. The calculated angles produced by the algorithm were evaluated using two methods. The first method is to use Symmetric Mean Absolute Percentage Error (SMAPE) to measure the relative error in percentage. The proposed method achieves a SMAPE of 10.76%, which outperforms the benchmarks. The second method is to calculate the absolute difference. The majority of the predictions had an error less than 5 degrees and 94.59% of predictions had error less than 10 degrees, indicating the reliability of the YOLACT model.
Conclusions: In comparison to previous methods, the YOLACT approach is reliable and consistent concerning SMAPE with low error rate for predictions. It also does not require an excessive amount of computation resources, demonstrating the promise of an ML tool to contribute to increased accuracy of Cobb angle measurements in the future daily practice of pediatric radiologists.
Session Info:

Posters - Scientific

Musculoskeletal

SPR Posters - Scientific

More abstracts on this topic:
Local experience in utilization of EOS biplanar X-ray imaging system in Asian paediatric patients

Cheung Kenneth, Lau Damian, Lam Wendy

Physeal Diffusion Tensor Tractography ROI Automation with a 3D Convolutional Neural Network

Mutasa Simukayi, Liu Michael, Duong Phuong, Jambawalikar Sachin, Mostoufi-moab Sogol, Jaramillo Diego

More abstracts from these authors:
Automated Cobb Angle Measurement for Assessment of Adolescence Scoliosis Using Augmented U-Net with Non-Square Kernels

Wu Yujie, Namdar Khashayar, Chen Chaojun, Hosseinpour Shahob, Shroff Manohar, Doria Andrea, Khalvati Farzad

Correlative Assessment of Machine Learning-Based Cobb Angle Measurements and Human-Based Measurements in Adolescent Idiopathic and Congenital Scoliosis

Stott Samantha, Wu Yujie, Hosseinpour Shahob, Chen Chaojun, Namdar Khashayar, Amirabadi Afsaneh, Shroff Manohar, Khalvati Farzad, Doria Andrea

Preview
Poster____SCI-022.pdf
You have to be authorized to contact abstract author. Please, Login or Signup.

Please note that this is a separate login, not connected with your credentials used for the SPR main website.

Not Available

Comments

We encourage you to join the discussion by posting your comments and questions below.

Presenters will be notified of your post so that they can respond as appropriate.

This discussion platform is provided to foster engagement, and stimulate conversation and knowledge sharing.

Please click here to review the full terms and conditions for engaging in the discussion, including refraining from product promotion and non-constructive feedback.

 

You have to be authorized to post a comment. Please, Login or Signup.

Please note that this is a separate login, not connected with your credentials used for the SPR main website.


   Rate this abstract  (Maximum characters: 500)