Ultrasound Aided Vertebral Level Localization for Lumbar Surgery

Localization of the correct vertebral level for surgical entry during lumbar hernia surgery is not straightforward. In this paper, we develop and evaluate a solution using free-hand 2-D ultrasound (US) imaging in the operation room (OR). Our system exploits the difference in spinous process shapes o...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2017-10, Vol.36 (10), p.2138-2147
Main Authors: Baka, Nora, Leenstra, Sieger, van Walsum, Theo
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Bone imaging
Bone segmentation
Bones
computer aided surgery
Deep learning
Feasibility studies
Hernia
Humans
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
Localization
Lumbar Vertebrae - diagnostic imaging
Lumbar Vertebrae - surgery
lumbar X-ray
Machine Learning
Male
Middle Aged
Neural networks
Shape
Shape recognition
spine
State of the art
Surgery
Surgery, Computer-Assisted - methods
surgical guidance
Two dimensional displays
Ultrasonic imaging
Ultrasonography - methods
Ultrasound
Vertebrae
X-ray imaging
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Summary:Localization of the correct vertebral level for surgical entry during lumbar hernia surgery is not straightforward. In this paper, we develop and evaluate a solution using free-hand 2-D ultrasound (US) imaging in the operation room (OR). Our system exploits the difference in spinous process shapes of the vertebrae. The spinous processes are pre-operatively outlined and labeled in a lateral lumbar X-ray of the patient. Then, in the OR the spinous processes are imaged with 2-D sagittal US, and are automatically segmented and registered with the X-ray shapes. After a small number of scanned vertebrae, the system robustly matches the shapes, and propagates the X-ray label to the US images. The main contributions of our work are: we propose a deep convolutional neural network-based bone segmentation algorithm from US imaging that outperforms state of the art methods in both performance and speed. We present a matching strategy that determines the levels of the spinal processes being imaged. And lastly, we evaluate the complete procedure on 19 clinical data sets from two hospitals, and two observers. The final labeling was correct in 92% of the cases, demonstrating the feasibility of US-based surgical entry point detection for spinal surgeries.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2017.2738612