Abstract
Medical Image Segmentation (MIS) includes diverse tasks, from bone to organ segmentation, each with its own challenges in finding the best segmentation model. The state-of-the-art AutoML-related MIS-framework nnU-Net automates many aspects of model configuration but remains constrained by fixed hyperparameters and heuristic design choices. As a full-AutoML framework for MIS, we propose Auto-nnU-Net, a novel nnU-Net variant enabling hyperparameter optimization (HPO), neural architecture search (NAS), and hierarchical NAS (HNAS). Additionally, we propose Regularized PriorBand to balance model accuracy with the computational resources required for training, addressing the resource constraints often faced in real-world medical settings that limit the feasibility of extensive training procedures. We evaluate our approach across diverse MIS datasets from the well-established Medical Segmentation Decathlon, analyzing the impact of AutoML techniques on segmentation performance, computational efficiency, and model design choices. The results demonstrate that our AutoML approach substantially improves the segmentation performance of nnU-Net on 6 out of 10 datasets and is on par on the other datasets while maintaining practical resource requirements. Our code is available at this https URL.
Abstract (translated)
医学图像分割（MIS）涵盖了从骨骼到器官的各种任务，每种任务在寻找最佳分割模型时都有其独特的挑战。目前最先进的与AutoML相关的MIS框架nnU-Net自动配置了许多模型参数，但仍受限于固定的超参数和启发式设计选择。作为面向MIS的全自动化机器学习（AutoML）框架，我们提出了一种新的nnU-Net变体——Auto-nnU-Net，该框架支持超参数优化（HPO）、神经架构搜索（NAS）以及分层NAS（HNAS）。此外，我们还提出了Regularized PriorBand方法，在保证模型精度的同时考虑训练所需的计算资源，解决了实际医疗环境中由于资源限制而使广泛培训程序不可行的问题。我们在Medical Segmentation Decathlon中一系列多样化的MIS数据集上评估了我们的方法，分析了AutoML技术对分割性能、计算效率以及模型设计选择的影响。结果表明，在10个数据集中，我们的AutoML方法在6个数据集中显著提高了nnU-Net的分割性能，并且在其他四个数据集中的表现与现有方法相当，同时保持实用的资源需求。 我们提出的代码可以在以下网址获取：[请在此处插入链接]。
URL
https://arxiv.org/abs/2505.16561
