Faculty Publications
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Item A more generalizable DNN based Automatic Segmentation of Brain Tumors from Multimodal low-resolution 2D MRI(Institute of Electrical and Electronics Engineers Inc., 2021) Bhaskaracharya, B.; Nair, R.P.; Prakashini, K.; Girish Menon, R.; Litvak, P.; Mandava, P.; Vijayasenan, D.; Sumam David, S.In the field of Neuro-oncology, there is a need for improved diagnosis and prognosis of brain tumors. Brain tumor segmentation is important for treatment planning and assessing the treatment outcomes. Manual segmentation of brain tumors is tedious, time-consuming, and subjective. In this work, an efficient encoder-decoder based architectures were implemented for automatic segmentation of brain tumors from low resolution 2D images. Ensemble of the multiple architectures (EMMA) improves the performance of the brain tumor segmentation. Furthermore, the computational requirements of the proposed models are lower than that of BraTS-challenge methods. The average Fl-scores on the BraTS-challenge validation dataset for Tumor Core, Whole Tumor, and Enhancing Tumor are 0.82, 0.87, and 0.78, respectively. The average Fl-scores on the KMC-Manipal dataset for TC, WT, and ET are 0.74, 0.82, and 0.68 respectively. © 2021 IEEE.Item A hybrid CNN-FC approach for automatic grading of brain tumors from non-invasive MRIs(Institute of Electrical and Electronics Engineers Inc., 2024) Bhaskaracharya, B.; Nair, R.P.; Prakashini, K.; Girish Menon, R.; Litvak, P.; Mandava, P.; Vijayasenan, D.; Sumam David, S.The grading of brain tumors is essential in treatment planning to effectively control the tumor growth and reduce the associated symptoms. Appropriate treatment planning might help in improving the quality of life and patient life span. Gliomas are indeed the most common type of brain tumor, originating from glial cells. Low-grade gliomas (grades 1 or 2) are typically slow-growing, less invasive, and may be suitable for surgical resection or targeted therapies. On the other hand, higher-grade tumors such as grades 3 or 4 are more aggressive, it might infiltrate the surrounding brain tissue making complete resection challenging. In clinical diagnosis, traditionally tumor grading requires the procedure of resecting a part of the tumor for microscopic examination. To address this, a method to grade the tumor non-invasively using MRIs is proposed. Our work utilized the BraTS2018 dataset to segment the substructure of brain tumors that includes necrosis and non-enhancing, edema, and enhancing regions. These regions are then used to train the proposed grading model. Furthermore, we evaluated the performance of our model on a tertiary hospital dataset consisting of 69 samples. The accuracy scores obtained on the BraTS2018 test sample and tertiary hospital dataset are 0.87 and, 0.85 respectively. This consistent score on both public and tertiary hospital datasets indicates a reliable and stable performance of the model. © 2024 IEEE.Item 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