Ramya Muthukrishnan

32-D474

32 Vassar St

Cambridge, MA 01239

I’m a first-year PhD student at MIT CSAIL in Dr. Polina Golland’s Medical Vision Group, supported by the MIT Abdul Latif Jameel Fellowship for Machine Learning and Health Solutions Fellowship.

I obtained my bachelors degree in computer science from the University of Pennsylvania, during which I worked with Dr. Brian Litt at the Center for Neuroengineering and Therapeutics and Drs. Spyros Bakas and Despina Kontos at the Center for Biomedical Image Computing & Analytics on deep learning solutions to automating 3D lesion segmentation in postsurgical epilepsy MRI and quantitative breast density estimation in mammography, respectively. I also received my masters degree in data science from Penn, where I completed my thesis on deploying graph neural networks for distributed control of multi-robot systems under the supervision of Dr. Alejandro Ribeiro. Additionally, I was fortunate enough to intern at MIT Lincoln Laboratory, where I researched neural networks for solving forward and inverse physics problems in the radar domain.

Outside of work, I enjoy staying active, spending time outdoors, traveling, and trying new coffee shops :)

news

Sep 6, 2023	I started my PhD at MIT CSAIL in the Medical Vision Group, directed by Dr. Polina Golland.
May 15, 2023	I graduated with a masters degree in data science from Penn.
May 4, 2023	My masters thesis, titled “Graph neural networks for scalable, real-world coverage control in distributed multi-robot systems”, was awarded first place in the data science department.
Feb 13, 2023	I was invited to give a talk on InvRT: Solving Radar Inverse Problems with Transformers, my work as an intern at MIT Lincoln Laboratory, at the 2nd Annual AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE).
Aug 12, 2022	My first first-author paper, Deep learning-based automated segmentation of resection cavities on postsurgical epilepsy MRI, was published in NeuroImage: Clinical

selected publications

2022

NeuroImage Clin

Deep learning-based automated segmentation of resection cavities on postsurgical epilepsy MRI

T. Campbell Arnold, Ramya Muthukrishnan, Akash R. Pattnaik, and 9 more authors

NeuroImage: Clinical, 2022

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Accurate segmentation of surgical resection sites is critical for clinical assessments and neuroimaging research applications, including resection extent determination, predictive modeling of surgery outcome, and masking image processing near resection sites. In this study, an automated resection cavity segmentation algorithm is developed for analyzing postoperative MRI of epilepsy patients and deployed in an easy-to-use graphical user interface (GUI) that estimates remnant brain volumes, including postsurgical hippocampal remnant tissue. This retrospective study included postoperative T1-weighted MRI from 62 temporal lobe epilepsy (TLE) patients who underwent resective surgery. The resection site was manually segmented and reviewed by a neuroradiologist (JMS). A majority vote ensemble algorithm was used to segment surgical resections, using 3 U-Net convolutional neural networks trained on axial, coronal, and sagittal slices, respectively. The algorithm was trained using 5-fold cross validation, with data partitioned into training (N = 27) testing (N = 9), and validation (N = 9) sets, and evaluated on a separate held-out test set (N = 17). Algorithm performance was assessed using Dice-Sørensen coefficient (DSC), Hausdorff distance, and volume estimates. Additionally, we deploy a fully-automated, GUI-based pipeline that compares resection segmentations with preoperative imaging and reports estimates of resected brain structures. The cross-validation and held-out test median DSCs were 0.84 ± 0.08 and 0.74 ± 0.22 (median ± interquartile range) respectively, which approach inter-rater reliability between radiologists (0.84–0.86) as reported in the literature. Median 95% Hausdorff distances were 3.6 mm and 4.0 mm respectively, indicating high segmentation boundary confidence. Automated and manual resection volume estimates were highly correlated for both cross-validation (r = 0.94, p < 0.0001) and held-out test subjects (r = 0.87, p < 0.0001). Automated and manual segmentations overlapped in all 62 subjects, indicating a low false negative rate. In control subjects (N = 40), the classifier segmented no voxels (N = 33), <50 voxels (N = 5), or a small volumes<0.5 cm3 (N = 2), indicating a low false positive rate that can be controlled via thresholding. There was strong agreement between postoperative hippocampal remnant volumes determined using automated and manual resection segmentations (r = 0.90, p < 0.0001, mean absolute error = 6.3 %), indicating that automated resection segmentations can permit quantification of postoperative brain volumes after epilepsy surgery. Applications include quantification of postoperative remnant brain volumes, correction of deformable registration, and localization of removed brain regions for network modeling.