Mr. Ashwin Kumar Profile

Ashwin Kumar

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Proceedings Article | 4 April 2022 Presentation + Paper

Mapping pediatric spinal cord development with age

Ashwin Kumar, Seth Smith, Bennett Landman, Kurt Schilling, Simon Vandekar, Aashim Bhatia

Proceedings Volume 12032, 1203213 (2022) https://doi.org/10.1117/12.2612210

KEYWORDS: Spinal cord, Spine, Brain, Image segmentation, Radiology, Pathology, Medicine

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Pediatric spinal cord morphometry has been relatively understudied because of non-optimal image quality due to the difficulty of spine imaging, rarity of post-mortem analysis, motion artifacts, and pediatric MR imaging research focus on understanding spinal injury or pathology. The pediatric brain has been comparatively well-studied with white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) differences observed with age and gender. Therefore, a greater understanding of pediatric cervical and thoracic spinal cord morphometry would be beneficial for developing clinically relevant cord growth models. We focused on retrospectively characterizing cervical and thoracic spinal cord growth and morphometry changes in a healthy pediatric population. High resolution multi-echo gradient echo (mFFE) images were acquired from pediatric spinal cord scans from 63 patients (mean: 9.24 years, range: 0.83-17.67 years). The mFFE scans were then registered to the template space for uniform viewing and analysis by using a customized semi-automatic processing pipeline involving Spinal Cord Toolbox (SCT). Jacobian control determinants were calculated, and subsequent WM, GM, dorsal column, lateral funiculi, and ventral funiculi scalar averaging was conducted. Random effects models were used to model age-related Jacobian scalar differences. Observing the growth of cord matter by patient age and vertebral level suggests that the upper cervical spinal cord, specifically C2-C3, and mid-thoracic spinal cord, T3-T8, grow faster than other cervical levels and thoracic levels, respectively. This knowledge will facilitate clinical decision making when considering spine interventions and conducting radiological analysis in children with cervical and thoracic spine abnormalities.

Proceedings Article | 15 February 2021 Presentation + Paper

Automated detection of surgical wounds in videos of open neck procedures using a mask R-CNN

Alexander Langerman, Benoit Dawant, TingYan Deng, Shubham Gulati, Ashwin Kumar, William Rodriguez

Proceedings Volume 11598, 1159817 (2021) https://doi.org/10.1117/12.2580908

KEYWORDS: Video, Neck, Visual process modeling, Surgery, Image quality, Visualization, Light sources and illumination, Endoscopy, Cameras

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Open surgery represents a dominant proportion of procedures performed, but has lagged behind endoscopic surgery in video-based insights due to the difficulty obtaining high-quality open surgical video. Automated detection of the open surgical wound would enhance tracking and stabilization of body-worn cameras to optimize video capture for these procedures. We present results using a mask R-CNN to identify the surgical wound (the “area of interest”, AOI) in image sets derived from 27 open neck procedures (a 2310-image training/validation set and a 1163-image testing set). Bounding box application to the surgical wound was reliable (F-1 > 0.905) in the testing sets with a <5% false positive rate (recognizing non-wound areas as the AOI). Mask application to greater than 50% of the wound area also had good success (F-1 = 0.831) under parameters set for high specificity. When applied to short video clips as proof-of-principle, the model performed well both with emerging AOI (i.e., identifying the wound as incisions were developed) and with recapture of the AOI following obstruction). Overall, we identified image lighting quality and the presence of distractors (e.g., bloody sponges) as the primary sources of model errors on visual review. These data serve as a first demonstration of open surgical wound detection using first-person video footage, and sets the stage for further work in this area.

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