Faculty Publications

Now showing 1 - 2 of 2

Computational evaluation of the effect of femoral component curvature on the mechanical response of the UHMWPE tibial insert in total knee replacement implants
(Elsevier Ltd, 2022) Raju, V.; Koorata, P.K.
Total knee replacement (TKR) surgery is done on individuals with end-stage osteoarthritis to restore knee function and alleviate joint discomfort. There have been recent developments in the design of customized implants based on patient-specific data obtained from MRI scans and subsequent image processing techniques. Here curvature of the femoral component plays an important role in effective implant design. Therefore, the objective here is to investigate the influence of this curvature of the femoral component on the mechanical response of the bearing component. A 3D finite element knee implant model with a circular and an elliptical femoral component is developed and investigated for gait kinetics and kinematics. Responses such as contact pressure, stresses, strains, and wear produced on the tibial insert are estimated throughout the gait cycle. These findings suggest that the elliptical femoral component generates less contact pressure on the tibial insert than its circular counterpart. It is also inferred that too much variation in this curvature is not recommended as it may affect the patient's comfort level. In addition, the wear of the tibial insert is computed based on the contact pressure created by both knee implant models. Our study suggests an optimum value for the curvature and the comfort level of the patients over the existing knee implant designs. © 2022 Elsevier Ltd. All rights reserved.
Generalizable DNN model for brain tumor sub-structure segmentation from low-resolution 2D multimodal MR Images
(Elsevier Ltd, 2025) Bhaskaracharya, B.; Nair, R.P.; Prakashini, K.; R, G.M.; Litvak, P.; Mandava, P.; Vijayasenan, D.; Sumam David, S.D.
Segmenting subregions within gliomas are critical for effective treatment planning of brain tumors. However, traditional methods of analyzing these regions using multiple MRI modalities are time-consuming, tedious, and subjective. To address these challenges, automatic segmentation models have been developed but are often built with complex 3D architecture using 3D MRI data. Also, brain tumor substructure segmentation is a highly class-imbalanced problem. To overcome these limitations, we propose two models that work on low-resolution 2D MRI data, widely used in resource-constrained countries. One model employs training a 2D U-NeT model using proposed hard sampling approach, demonstrating its effectiveness in segmenting gliomas, especially in datasets with extreme class imbalance. Another model incorporates pointwise and depthwise convolutions in each convolutional layer, enabling efficient information processing and feature learning. By ensembling the prediction maps of these models, we further improve overall segmentation performance. Our models were evaluated on the BraTS2018 dataset, achieving dice scores of 0.78 for Enhancing Tumor (ET), 0.82 for Tumor Core (TC), and 0.87 for Whole Tumor (WT). On a tertiary care hospital dataset, dice scores of 0.68 (ET), 0.75 (TC), and 0.84 (WT) were obtained, demonstrating their robustness and proximity to state-of-the-art methods. In summary, the proposed models offer efficient and reliable segmentation of glioma subregions. Their high dice scores, and computational efficiency, make them valuable tools for treatment planning and advancements in brain tumor segmentation. © 2024 Elsevier Ltd

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