Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Girish Menon, R.

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    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.
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    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.
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    Artery Vein Segmentation in Handheld Fundus Camera Retinal Images and leveraging Cross Entropy for improved Semantic performance
    (Institute of Electrical and Electronics Engineers Inc., 2024) Yohannan, R.P.; Sumam David, S.; Vijayasenan, D.; Chowdary, R.T.; Girish Menon, R.; Menon, S.G.
    The segmentation of retinal vessels into arteries and veins in retinal images is a crucial task for analysing the vascular changes with respect to many diseases that manifest ocular symptoms. But most existing research has concentrated on fundus images acquired using tabletop cameras and not much has been studied on images captured by handheld cameras. Such cameras are particularly useful for examining bedridden patients, especially those who may have conditions such as hypertension or diabetes that can affect the retina, since they are portable and can be easily maneuvered by healthcare providers, allowing them to perform retinal examinations conveniently at the patient's bedside. This paper presents an approach to segment such images and assesses the impact of data augmentation on model performance. It further presents a method to compute pixel level weights during training, that allows for fine-grained adjustment of the loss function. © 2024 IEEE.