Dr. Sumit K. Agrawal Profile

Dr. Sumit K. Agrawal

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Proceedings Article | 28 February 2020 Paper

Fully automated segmentation of the temporal bone from micro-CT using deep learning

Soodeh Nikan, Sumit K. Agrawal, Hanif Ladak

Proceedings Volume 11317, 113171U (2020) https://doi.org/10.1117/12.2549609

KEYWORDS: Convolutional neural networks, Surgery, Simulations, Image segmentation, 3D modeling, Computed tomography, Ear, Virtual reality, 3D image processing

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The temporal bone is a part of the lateral skull surface that contains organs responsible for hearing and balance. Mastering surgery of the temporal bone is challenging because of this complex and microscopic three-dimensional (3D) anatomy. Virtual reality (VR) surgical simulators have proven to be effective for surgical training. In this paper a fully automated method is proposed for segmenting multiple temporal-bone structures based on micro computed tomography (micro-CT) images for a realistic virtual environment. An automated segmentation pipeline is proposed based on a three-dimensional, fully convolutional neural network. The proposed balanced subsampling strategy creates balanced learning among the labels of multiple anatomical structures and reduces the class imbalance. The accuracy and speed of the proposed algorithm outperforms current manual and semi-automated segmentation techniques. The average Dice similarity scores for all temporal-bone structures was 88%. The proposed algorithm was validated on low-resolution CTs scanned by other centers with different scanner parameters than the ones used to create the algorithm. The presented fully automated segmentation algorithm creates 3D models of multiple structures of temporal-bone anatomy from micro- CT images with sufficient accuracy to be used in VR surgical training simulators.

Proceedings Article | 24 February 2017 Paper

Evaluation of non-rigid registration parameters for atlas-based segmentation of CT images of human cochlea

Mai Elfarnawany, S. Riyahi Alam, Sumit Agrawal, Hanif Ladak

Proceedings Volume 10133, 101330Z (2017) https://doi.org/10.1117/12.2254040

KEYWORDS: Visualization, Surgery, Electrodes, Image segmentation, Error analysis, Image resolution, Image registration, Computed tomography, Rigid registration, Algorithm development

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Cochlear implant surgery is a hearing restoration procedure for patients with profound hearing loss. In this surgery, an electrode is inserted into the cochlea to stimulate the auditory nerve and restore the patient’s hearing. Clinical computed tomography (CT) images are used for planning and evaluation of electrode placement, but their low resolution limits the visualization of internal cochlear structures. Therefore, high resolution micro-CT images are used to develop atlas-based segmentation methods to extract these nonvisible anatomical features in clinical CT images. Accurate registration of the high and low resolution CT images is a prerequisite for reliable atlas-based segmentation. In this study, we evaluate and compare different non-rigid B-spline registration parameters using micro-CT and clinical CT images of five cadaveric human cochleae. The varying registration parameters are cost function (normalized correlation (NC), mutual information and mean square error), interpolation method (linear, windowed-sinc and B-spline) and sampling percentage (1%, 10% and 100%). We compare the registration results visually and quantitatively using the Dice similarity coefficient (DSC), Hausdorff distance (HD) and absolute percentage error in cochlear volume. Using MI or MSE cost functions and linear or windowed-sinc interpolation resulted in visually undesirable deformation of internal cochlear structures. Quantitatively, the transforms using 100% sampling percentage yielded the highest DSC and smallest HD (0.828±0.021 and 0.25±0.09mm respectively). Therefore, B-spline registration with cost function: NC, interpolation: B-spline and sampling percentage: moments 100% can be the foundation of developing an optimized atlas-based segmentation algorithm of intracochlear structures in clinical CT images.

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