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Life sciences · Preprint
arXiv · September 9, 2026
Early or partial results. Treat as a signal, not a conclusion.
LightMedSeg-ISLES is a lightweight convolutional segmentation model achieving 97.5% of a much larger nnU-Net's performance on stroke lesion segmentation while requiring 81-fold fewer parameters and 4.7-fold fewer floating-point operations. This represents a technical proof of concept for computational efficiency in medical image segmentation, but clinical utility, generalizability, and independent validation remain unevaluated.
Algorithm validation on held-out test cohort. 146-case held-out cohort from ISLES'26 stroke lesion segmentation challenge; T1-weighted MRI imaging.. Intervention: LightMedSeg-ISLES segmentation pipeline (1.26M parameters) with four-pass flip test-time augmentation. Compared with: nnU-Net ResEnc-L (102.35M parameters, size-filtered), UNETR++, and nnFormer. n = 146. ISLES'26 challenge (specific centers not detailed).
LightMedSeg mean Dice of 0.618 with test-time augmentation on 146-case held-out cohort, versus nnU-Net Dice 0.634 after size filtering LightMedSeg lesion-wise F1 of 0.599 exceeds nnU-Net's 0.544 by 0.055 LightMedSeg uses 1.26 million parameters versus nnU-Net ResEnc-L's 102.35 million parameters (81.4× reduction)
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If validated independently and prospectively, LightMedSeg could enable real-time or resource-constrained deployment of stroke lesion segmentation in settings with limited computational infrastructure. However, clinical utility—such as impact on diagnosis, prognosis, or treatment planning—has not been established and requires prospective evaluation.
Single-center technical validation of a novel segmentation architecture on a held-out cohort, comparing computational efficiency and performance metrics without independent test set or clinical outcome evaluation.
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If validated independently and prospectively, LightMedSeg could enable real-time or resource-constrained deployment of stroke lesion segmentation in settings with limited computational infrastructure. However, clinical utility—such as impact on diagnosis, prognosis, or treatment planning—has not been established and requires prospective evaluation.
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Large networks and ensembles often lead medical image segmentation challenges, but their storage and inference demands complicate deployment. We present LightMedSeg-ISLES, a 1.26-million-parameter pipeline for T1-weighted stroke lesion segmentation in ISLES'26. On a 146-case held-out cohort, flip test-time augmentation produces 0.618 mean Dice and 0.599 lesion-wise F1. A 102.35-million-parameter nnU-Net ResEnc-L produces 0.634 Dice and 0.544 lesion-wise F1 after size filtering. LightMedSeg therefore retains 97.5\% of nnU-Net's Dice with 81.4$\times$ fewer parameters while improving lesion-wise F1 by 0.055. Its four-pass TTA operating point requires 4.7$\times$ fewer FLOPs per standardized patch than nnU-Net. It also slightly exceeds filtered UNETR++ and nnFormer. Longer training and stronger augmentation add 0.0358 Dice without increasing capacity, establishing a strong single-checkpoint alternative to much larger models.
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