Spatiotemporal Glioblastoma Evolution Visual Prediction

Research at The ∀ Lab & Image Science Lab

paused Medical Imaging Neural ODEs MRI Forecasting
Project Website Source Code

project status: currently paused (January - September 2025)

Conducted during the Spring and Summer semesters of 2025 under Dr. Pulkit Grover, Dr. Aswin Sankaranarayanan and Dr. Matthew J Shepard, MD, this study is complementary to a Partial Differential Equation based approach by Cynthia Han a fellow graduate student researcher. Both approaches use a multi-modal approach that combine FLAIR, T1, T2 and CT1 modalities of brain MRI from the LUMIERE dataset.

I worked on a Neural Ordinary Differential (N-ODE) network framework designed to model and predict tumor growth dynamics from longitudinal MRI data. By encoding a patient’s tumor size measurements over time into interpretable kinetic parameters, the N-ODE formulates tumor progression as a continuous-time dynamical system, allowing for unbiased and personalized predictions, even from early-stage data.

prelimnary results of my model (actually!) predicting a future timepoint of the tumour growth across all four modalities
  • the model uses a encoder-neural ordinary DE-decoder model with a combined loss approach, integrating Mean Squared Error to match anatomical details and Dice Loss to ensure accurate tumor segmentation, thus optimizing both image fidelity.
  • the model’s encoder-decoder architecture not only improves future tumor size predictions but also produces metrics that strongly predict overall survival.
me presenting this work at carnegie mellon's 2025 forum by the biomedical engineering department

This project evolved through a few related modeling directions.

The earliest recovered version was a rough Neural ODE prototype built from the original source code. That branch established the basic idea of using an attention U-Net encoder, temporal conditioning, and a latent ODE block for glioblastoma forecasting.

I then cleaned that into a runnable neural-ode-implementation branch. That version turned the recovered idea into a proper pipeline for the local patient data, using a 2D slice-based attention U-Net + Neural ODE model, strict holdout evaluation, and a persistence baseline for comparison.

After that, I shifted to a history-conditioned-forecast branch that reframed the task as prefix-history prediction: use all earlier MRI weeks for a patient and forecast the next one. In that version, each historical week is encoded separately, a learned week embedding is added, the latent history is aggregated, and the Neural ODE evolves the state forward in continuous time before decoding the future scan.

The history-conditioned-forecast-slim branch is a slimmer merged version of that same prefix-history line, keeping the forecasting idea while trimming the branch down.

In parallel, there was also a separate physics-dual-patient-rerun branch that explored a physics-informed 3D forecasting pipeline. That approach treated the MRI evolution as a more explicit dynamics problem rather than a learned latent forecast, and it used the same local patient set for comparison.

Across all of these branches, the common goal was the same: model glioblastoma progression from longitudinal MRI data and compare learned forecasts against simple persistence baselines.