Automated Instance Segmentation and Keypoint Detection for Spine Imaging Analysis

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Automated Instance Segmentation and Keypoint Detection for Spine Imaging Analysis


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Automated Instance Segmentation and Keypoint Detection for Spine Imaging Analysis

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1.INTRODUCTION Individuals diagnosed with degenerative bone diseases such as osteoporosis are more susceptible to vertebral fractures which comprise almost 50% of all osteoporotic fractures in the United States per year. Thoracolumbar vertebral body fractures can be classified as wedge, biconcave, or crush, depending on the anterior (Ha), middle (Hm), and posterior (Hp) heights of each vertebral body. However, determining these height measurements in clinical workflow is time-consuming and resource intensive. Instance segmentation and keypoint detection network designs offer the ability to determine Ha, Hm, and Hp, in order to classify deformities according to the semi- or fully quantitative method. We investigated the accuracy of such algorithms for analyzing sagittal spine CT and MR images. 2.MATERIALS AND METHODS Sagittal spine MRI (998) and CT scans (35) were split into training and testing data. The training set was augmented (?15?rotation, ?30% contrast and brightness, random cropping) to a final size of 5667 vertebrae (1269 CT & 4398 MR). A testing set of 238 MR and 15 CT scans was used to evaluate the neural network. Mask RCNN (architecture for basic instance segmentation) was modified to include a 2D- UNet head (for better segmentation) along with a Keypoint RCNN network to detect 6 relevant vertebral keypoints (network design in Figure 1 w/one network per imaging modality). The effectiveness of the neural network was measured using two parameters: Dice score (ranges from 0 to 1 where 1=predicted overlay is manually segmented ground truth) and keypoint error distance (distance of predicted key-point location compared to manually labeled reference point). 3.RESULTS The neural network achieved an overall Dice coefficient of 0.968 and key-point error distance of 0.984 millimeters on the testing dataset. Mean percent error in Ha, Hm, and Hp height calculations (based on keypoints) was 0.13%.The neural network was able to process each scan slice with a mean time of 1.492 seconds. 4.CONCLUSIONS The neural network was able to determine morphometric measurements for detecting spinal fractures with high accuracy on sagittal MR and CT images. This approach could simplify the screening, detection of changes, and surgical planning in patients with vertebral deformities and fractures by reducing the burden on radiologists who have to do measurements manually.
1.INTRODUCTION Individuals diagnosed with degenerative bone diseases such as osteoporosis are more susceptible to vertebral fractures which comprise almost 50% of all osteoporotic fractures in the United States per year. Thoracolumbar vertebral body fractures can be classified as wedge, biconcave, or crush, depending on the anterior (Ha), middle (Hm), and posterior (Hp) heights of each vertebral body. However, determining these height measurements in clinical workflow is time-consuming and resource intensive. Instance segmentation and keypoint detection network designs offer the ability to determine Ha, Hm, and Hp, in order to classify deformities according to the semi- or fully quantitative method. We investigated the accuracy of such algorithms for analyzing sagittal spine CT and MR images. 2.MATERIALS AND METHODS Sagittal spine MRI (998) and CT scans (35) were split into training and testing data. The training set was augmented (?15?rotation, ?30% contrast and brightness, random cropping) to a final size of 5667 vertebrae (1269 CT & 4398 MR). A testing set of 238 MR and 15 CT scans was used to evaluate the neural network. Mask RCNN (architecture for basic instance segmentation) was modified to include a 2D- UNet head (for better segmentation) along with a Keypoint RCNN network to detect 6 relevant vertebral keypoints (network design in Figure 1 w/one network per imaging modality). The effectiveness of the neural network was measured using two parameters: Dice score (ranges from 0 to 1 where 1=predicted overlay is manually segmented ground truth) and keypoint error distance (distance of predicted key-point location compared to manually labeled reference point). 3.RESULTS The neural network achieved an overall Dice coefficient of 0.968 and key-point error distance of 0.984 millimeters on the testing dataset. Mean percent error in Ha, Hm, and Hp height calculations (based on keypoints) was 0.13%.The neural network was able to process each scan slice with a mean time of 1.492 seconds. 4.CONCLUSIONS The neural network was able to determine morphometric measurements for detecting spinal fractures with high accuracy on sagittal MR and CT images. This approach could simplify the screening, detection of changes, and surgical planning in patients with vertebral deformities and fractures by reducing the burden on radiologists who have to do measurements manually.