Faculty, Staff and Student Publications
Language
English
Publication Date
6-3-2026
Journal
npj Health Systems
Abstract
Deploying medical image segmentation models in routine clinical workflows is often constrained by on-premises infrastructure, where computational resources are fixed and cloud-based inference may be restricted by governance and security policies. While high-capacity models achieve strong segmentation accuracy, their computational demands hinder practical deployment and long-term maintainability in hospital environments. We present a deployment-oriented framework that leverages knowledge distillation to translate a high-performing segmentation model into a scalable family of compact student models without modifying the inference pipeline. The framework is primarily evaluated on nnU-Net, with additional validation across transformer and heterogeneous teacher–student architectures. The proposed approach preserves architectural compatibility with existing clinical systems while enabling systematic capacity reduction. We evaluate framework on a multi-site brain MRI dataset comprising 1104 3D volumes, with independent testing on 101 curated cases, and is further examined on abdominal CT to assess cross-modality generalizability. Under aggressive parameter reduction (94%), the distilled student model preserves nearly all of the teacher’s segmentation accuracy (98.7%), while achieving substantial efficiency gains, including up to a 67% reduction in CPU inference latency without additional deployment overhead. These results demonstrate that knowledge distillation provides a practical and reliable pathway for converting research-grade segmentation models into maintainable, deployment-ready components for on-premises clinical workflows in real-world health systems.
Published Open-Access
yes
Recommended Citation
Lan, Qizhen; Choi, Aaron; Ma, Jun; et al., "From Performance to Practice: Knowledge-Distilled Segmentator for On-Premises Clinical Workflows" (2026). Faculty, Staff and Student Publications. 874.
https://digitalcommons.library.tmc.edu/uthshis_docs/874