小数据下简单模型比大模型更适配干细胞图像分割。
Semantic Segmentation of iPS Cells: Case Study on Model Complexity in Biomedical Imaging
- 用调优的DeepLabv3处理低对比度干细胞边界
- 在实验中超越SAM2等大模型，准确率更高
- 适合再生医学领域的小样本分割任务
医学图像分割不仅需要高精度，还需在复杂成像条件下保持鲁棒性。本研究表明，在特定实验条件下，经过精心配置的DeepLabv3模型可在诱导多能干细胞（iPS）细胞集落分割任务中取得优异表现，且优于无结构修改的大型基础模型SAM2及其医学变体MedSAM2。结果表明，对于具有细微、低对比度边界的专门任务，模型复杂度提升并不必然带来性能提升。本工作重新审视了更大更通用架构始终更优的假设，证明经适当调整的简化模型在特定生物医学应用中可提供强准确性与实际可靠性。我们还开源了包含小样本策略和领域特定编码的实现，旨在推动再生医学等领域语义分割的进一步发展。
原文摘要 · Abstract (English)
Medical image segmentation requires not only accuracy but also robustness under challenging imaging conditions. In this study, we show that a carefully configured DeepLabv3 model can achieve high performance in segmenting induced pluripotent stem (iPS) cell colonies, and, under our experimental conditions, outperforms large-scale foundation models such as SAM2 and its medical variant MedSAM2 without structural modifications. These results suggest that, for specialized tasks characterized by subtle, low-contrast boundaries, increased model complexity does not necessarily translate to better performance. Our work revisits the assumption that ever-larger and more generalized architectures are always preferable, and provides evidence that appropriately adapted, simpler models may offer strong accuracy and practical reliability in domain-specific biomedical applications. We also offer an open-source implementation that includes strategies for small datasets and domain-specific encoding, with the aim of supporting further advances in semantic segmentation for regenerative medicine and related fields.
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