Mingli Chen, PhD

To develop a WEb-Accessible comprehensiVE (WEAVE) platform, that combines AI-driven segmentation with a user-friendly interface to automatically segment Vestibular Schwannomas (VS) and track longitudinal volumes for disease assessment, planning, and monitorin...

To address fragmentation and variability in longitudinal brain lesion assessment, we developed Brain-Dynamics, a vendor-neutral platform integrating auto‑segmentation, multimodal co‑registration, lesion labeling/tracking, and quantitative analytics for resear...

In MR-only radiotherapy planning (MROP), limited field-of-view (LFOV) acquisition and imaging artifacts can introduce truncated anatomy and density inaccuracies, reducing the reliability of dose calculation and preventing comprehensive plan evaluation due to...

DeepTuning is a deep learning–based dose prediction framework that generates multiple dose distributions with different trade-offs, analogous to multi-criteria optimization. Leveraging historical cases with varying trade-offs, DeepTuning extracts semantic tra...

Proton arc therapy (PAT) holds promise for enhancing plan conformity while minimizing dose sparing. However, PAT faces practical barriers related to delivery inefficiency, and its effectiveness can be compromised by beam range and radiobiological uncertaintie...

Adaptive radiotherapy (ART) and personalized ultra‑fractionated stereotactic adaptive radiotherapy (PULSAR) require longitudinal anatomical modeling, deformable image registration (DIR), and dose recalculation and accumulation. While these capabilities are we...

Accurately modeling how organs move is essential in modern medical imaging and radiotherapy. Tasks such as deformable image registration (DIR), dose accumulation, motion tracking, and treatment adaptation all depend on reliable representations of anatomical d...

Deep learning–based glioma segmentation models are commonly developed under the assumption that the standard glioma MRI protocol—T1-weighted, contrast-enhanced T1 (T1ce), T2-weighted, and FLAIR—is available; however, this assumption may not hold in time-const...

The goal of this study is to accurately estimate 3D deformation and generate real-time volumetric images using only one or two X-ray projections, overcoming the limitations of ultra-sparse conditions in conventional deformable image registration (DIR) and vol...

Motion estimation naturally requires two time points, making it inherently a relational quantity. This constrains how motion can be learned during training, as the entanglement of paired data makes cross-modality and cross-dimensional scenarios particularly c...