Bio


Dr. Shailja is a Postdoctoral researcher in the Radiological Science Laboratory at Stanford. She recently completed her PhD in Electrical and Computer Engineering at the University of California, Santa Barbara. Her research vision is to model healthcare data for precise diagnostics using AI and to integrate domain knowledge to "close the loop" between surgeons, physicians, and scientists. Her Ph.D. dissertation focused on developing a principled approach to model the white matter pathways in the human brain to analyze the topology of brain connections. At the Radiological Science Laboratory, she will primarily focus on mapping MRI structural and functional connectivity imaging data with electrophysiological measurements in the same patients.

Honors & Awards


  • Lancaster Best Dissertation Award, University of California, Santa Barbara (UCSB) (2024)
  • Certificate in College and University Teaching (CCUT), UCSB (2024)
  • Fiona and Michael Goodchild Graduate Mentoring Award, UCSB Graduate Division (2023)
  • NSF iREDEFINE Fellow, National Science Foundation, at the ECE Department Heads Association Annual Conference (2023)
  • ECE Dissertation Fellowship, Electrical and Computer Engineering, UCSB (2024)
  • Individualized Professional Skills Grant, UCSB (2024)
  • Travel Award, IPAM, MICCAI, ECEDHA iREDEFINE workshop, and NeurIPS (2021-24)
  • Undergraduate Academic Scholarship, Indian Institute of Technology (IIT), Kharagpur (2012)

Boards, Advisory Committees, Professional Organizations


  • Co-organizer, Computational Diffusion MRI (CDMRI) Workshop, MICCAI (2024 - Present)
  • Advisory Member, MICCAI Student Board (2024 - Present)

Professional Education


  • PhD, University of California, Santa Barbara, Department of Electrical and Computer Engineering (2024)
  • BTech, Indian Institute of Technology (IIT), Kharagpur, Department of Electrical Engineering (2016)

Stanford Advisors


All Publications


  • Artificial Intelligence for Automatic Analysis of Shunt Treatment in Presurgery and Postsurgery Computed Tomography Brain Scans of Patients With Idiopathic Normal Pressure Hydrocephalus. Neurosurgery Shailja, S., Nguyen, C., Thanigaivelan, K., Gudavalli, C., Bhagavatula, V., Chen, J. W., Manjunath, B. S. 2024

    Abstract

    Ventriculo-peritoneal shunt procedures can improve idiopathic normal pressure hydrocephalus (iNPH) symptoms. However, there are no automated methods that quantify the presurgery and postsurgery changes in the ventricular volume for computed tomography scans. Hence, the main goal of this research was to quantify longitudinal changes in the ventricular volume and its correlation with clinical improvement in iNPH symptoms. Furthermore, our objective was to develop an end-to-end graphical interface where surgeons can directly drag-drop a brain scan for quantified analysis.A total of 15 patients with 47 longitudinal computed tomography scans were taken before and after shunt surgery. Postoperative scans were collected between 1 and 45 months. We use a UNet-based model to develop a fully automated metric. Center slices of the scan that are most representative (80%) of the ventricular volume of the brain are used. Clinical symptoms of gait, balance, cognition, and bladder continence are studied with respect to the proposed metric.Fifteen patients with iNPH demonstrate a decrease in ventricular volume (as shown by our metric) postsurgery and a concurrent clinical improvement in their iNPH symptomatology. The decrease in postoperative central ventricular volume varied between 6 cc and 33 cc (mean: 20, SD: 9) among patients who experienced improvements in gait, bladder continence, and cognition. Two patients who showed improvement in only one or two of these symptoms had <4 cc of cerebrospinal fluid drained. Our artificial intelligence-based metric and the graphical user interface facilitate this quantified analysis.Proposed metric quantifies changes in ventricular volume before and after shunt surgery for patients with iNPH, serving as an automated and effective radiographic marker for a functioning shunt in a patient with iNPH.

    View details for DOI 10.1227/neu.0000000000003015

    View details for PubMedID 38842320

  • ReeBundle: A Method for Topological Modeling of White Matter Pathways Using Diffusion MRI. IEEE transactions on medical imaging Shailja, S., Bhagavatula, V., Cieslak, M., Vettel, J. M., Grafton, S. T., Manjunath, B. S. 2023; 42 (12): 3725-3737

    Abstract

    Tractography can generate millions of complex curvilinear fibers (streamlines) in 3D that exhibit the geometry of white matter pathways in the brain. Common approaches to analyzing white matter connectivity are based on adjacency matrices that quantify connection strength but do not account for any topological information. A critical element in neurological and developmental disorders is the topological deterioration and irregularities in streamlines. In this paper, we propose a novel Reeb graph-based method "ReeBundle" that efficiently encodes the topology and geometry of white matter fibers. Given the trajectories of neuronal fiber pathways (neuroanatomical bundle), we re-bundle the streamlines by modeling their spatial evolution to capture geometrically significant events (akin to a fingerprint). ReeBundle parameters control the granularity of the model and handle the presence of improbable streamlines commonly produced by tractography. Further, we propose a new Reeb graph-based distance metric that quantifies topological differences for automated quality control and bundle comparison. We show the practical usage of our method using two datasets: (1) For International Society for Magnetic Resonance in Medicine (ISMRM) dataset, ReeBundle handles the morphology of the white matter tract configurations due to branching and local ambiguities in complicated bundle tracts like anterior and posterior commissures; (2) For the longitudinal repeated measures in the Cognitive Resilience and Sleep History (CRASH) dataset, repeated scans of a given subject acquired weeks apart lead to provably similar Reeb graphs that differ significantly from other subjects, thus highlighting ReeBundle's potential for clinical fingerprinting of brain regions.

    View details for DOI 10.1109/TMI.2023.3306049

    View details for PubMedID 37590108

  • Segmentation, tracking, and sub-cellular feature extraction in 3D time-lapse images. Scientific reports Jiang, J., Khan, A., Shailja, S., Belteton, S. A., Goebel, M., Szymanski, D. B., Manjunath, B. S. 2023; 13 (1): 3483

    Abstract

    This paper presents a method for time-lapse 3D cell analysis. Specifically, we consider the problem of accurately localizing and quantitatively analyzing sub-cellular features, and for tracking individual cells from time-lapse 3D confocal cell image stacks. The heterogeneity of cells and the volume of multi-dimensional images presents a major challenge for fully automated analysis of morphogenesis and development of cells. This paper is motivated by the pavement cell growth process, and building a quantitative morphogenesis model. We propose a deep feature based segmentation method to accurately detect and label each cell region. An adjacency graph based method is used to extract sub-cellular features of the segmented cells. Finally, the robust graph based tracking algorithm using multiple cell features is proposed for associating cells at different time instances. We also demonstrate the generality of our tracking method on C. elegans fluorescent nuclei imagery. Extensive experiment results are provided and demonstrate the robustness of the proposed method. The code is available on GitHub and the method is available as a service through the BisQue portal.

    View details for DOI 10.1038/s41598-023-29149-z

    View details for PubMedID 36859457

    View details for PubMedCentralID PMC9977871

  • ReTrace: Topological Evaluation of White Matter Tractography Algorithms Using Reeb Graphs Shailja, S., Chen, J. W., Grafton, S. T., Manjunath, B. S., Mito, R., Powell, E., Rheault, F., Winzeck, S., Karaman, M. SPRINGER INTERNATIONAL PUBLISHING AG. 2023: 177-191
  • Erratum: Automatic classification and neurotransmitter prediction of synapses in electron microscopy - CORRIGENDUM. Biological imaging Zhang, A., Shailja, S., Borba, C., Miao, Y., Goebel, M., Ruschel, R., Ryan, K., Smith, W., Manjunath, B. S. 2023; 3: e1

    Abstract

    [This corrects the article DOI: 10.1017/S2633903X2200006X.].

    View details for DOI 10.1017/S2633903X23000016

    View details for PubMedID 38487688

  • Automatic classification and neurotransmitter prediction of synapses in electron microscopy. Biological imaging Zhang, A., Shailja, S., Borba, C., Miao, Y., Goebel, M., Ruschel, R., Ryan, K., Smith, W., Manjunath, B. S. 2022; 2: e6

    Abstract

    This paper presents a deep-learning-based workflow to detect synapses and predict their neurotransmitter type in the primitive chordate Ciona intestinalis (Ciona) electron microscopic (EM) images. Identifying synapses from EM images to build a full map of connections between neurons is a labor-intensive process and requires significant domain expertise. Automation of synapse classification would hasten the generation and analysis of connectomes. Furthermore, inferences concerning neuron type and function from synapse features are in many cases difficult to make. Finding the connection between synapse structure and function is an important step in fully understanding a connectome. Class Activation Maps derived from the convolutional neural network provide insights on important features of synapses based on cell type and function. The main contribution of this work is in the differentiation of synapses by neurotransmitter type through the structural information in their EM images. This enables the prediction of neurotransmitter types for neurons in Ciona, which were previously unknown. The prediction model with code is available on GitHub.

    View details for DOI 10.1017/S2633903X2200006X

    View details for PubMedID 38486830

    View details for PubMedCentralID PMC10936391

  • SEMI SUPERVISED SEGMENTATION AND GRAPH-BASED TRACKING OF 3D NUCLEI IN TIME-LAPSE MICROSCOPY Shailja, S., Jiang, J., Manjunath, B. S., IEEE IEEE. 2021: 385-389
  • A Computational Geometry Approach for Modeling Neuronal Fiber Pathways Shailja, S., Zhang, A., Manjunath, B. S., DeBruijne, M., Cattin, P. C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. SPRINGER INTERNATIONAL PUBLISHING AG. 2021: 175-185

    Abstract

    We propose a novel and efficient algorithm to model high-level topological structures of neuronal fibers. Tractography constructs complex neuronal fibers in three dimensions that exhibit the geometry of white matter pathways in the brain. However, most tractography analysis methods are time consuming and intractable. We develop a computational geometry-based tractography representation that aims to simplify the connectivity of white matter fibers. Given the trajectories of neuronal fiber pathways, we model the evolution of trajectories that encodes geometrically significant events and calculate their point correspondence in the 3D brain space. Trajectory inter-distance is used as a parameter to control the granularity of the model that allows local or global representation of the tractogram. Using diffusion MRI data from Alzheimer's patient study, we extract tractography features from our model for distinguishing the Alzheimer's subject from the normal control. Software implementation of our algorithm is available on GitHub (https://github.com/UCSB-VRL/ReebGraph.

    View details for DOI 10.1007/978-3-030-87237-3_17

    View details for Web of Science ID 000712019200017

    View details for PubMedID 34729555

    View details for PubMedCentralID PMC8560085

  • Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information. Frontiers in neuroscience Kao, P. Y., Shailja, S., Jiang, J., Zhang, A., Khan, A., Chen, J. W., Manjunath, B. S. 2019; 13: 1449

    Abstract

    The manual brain tumor annotation process is time consuming and resource consuming, therefore, an automated and accurate brain tumor segmentation tool is greatly in demand. In this paper, we introduce a novel method to integrate location information with the state-of-the-art patch-based neural networks for brain tumor segmentation. This is motivated by the observation that lesions are not uniformly distributed across different brain parcellation regions and that a locality-sensitive segmentation is likely to obtain better segmentation accuracy. Toward this, we use an existing brain parcellation atlas in the Montreal Neurological Institute (MNI) space and map this atlas to the individual subject data. This mapped atlas in the subject data space is integrated with structural Magnetic Resonance (MR) imaging data, and patch-based neural networks, including 3D U-Net and DeepMedic, are trained to classify the different brain lesions. Multiple state-of-the-art neural networks are trained and integrated with XGBoost fusion in the proposed two-level ensemble method. The first level reduces the uncertainty of the same type of models with different seed initializations, and the second level leverages the advantages of different types of neural network models. The proposed location information fusion method improves the segmentation performance of state-of-the-art networks including 3D U-Net and DeepMedic. Our proposed ensemble also achieves better segmentation performance compared to the state-of-the-art networks in BraTS 2017 and rivals state-of-the-art networks in BraTS 2018. Detailed results are provided on the public multimodal brain tumor segmentation (BraTS) benchmarks.

    View details for DOI 10.3389/fnins.2019.01449

    View details for PubMedID 32038146

    View details for PubMedCentralID PMC6993565