互動式醫學影像分割：基準資料集與基準線。

Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline

November 19, 2024

作者: Junlong Cheng, Bin Fu, Jin Ye, Guoan Wang, Tianbin Li, Haoyu Wang, Ruoyu Li, He Yao, Junren Chen, JingWen Li, Yanzhou Su, Min Zhu, Junjun He

cs.AI

摘要

互動式醫學影像分割（IMIS）長期受限於大規模、多樣化和密集標註數據集的有限可用性，這阻礙了模型泛化和在不同模型間一致評估的進展。本文介紹了IMed-361M基準數據集，這是對一般IMIS研究的重大進展。首先，我們從多個數據來源收集並標準化了超過640萬張醫學影像及其對應的地面真實遮罩。然後，利用視覺基礎模型的強大物體識別能力，我們自動為每個影像生成了密集的互動式遮罩，並通過嚴格的質量控制和細粒度管理確保其質量。IMed-361M不同於以往受特定模態或稀疏標註限制的數據集，它涵蓋了14種模態和204個分割目標，總計361百萬個遮罩，平均每張影像有56個遮罩。最後，我們在該數據集上開發了一個IMIS基線網絡，支持通過互動輸入（包括點擊、邊界框、文本提示及其組合）進行高質量遮罩生成。我們從多個角度評估了其在醫學影像分割任務中的性能，展示了與現有互動式分割模型相比的優越準確性和可擴展性。為促進醫學計算機視覺基礎模型的研究，我們在https://github.com/uni-medical/IMIS-Bench 上發布了IMed-361M數據集和模型。

English

Interactive Medical Image Segmentation (IMIS) has long been constrained by the limited availability of large-scale, diverse, and densely annotated datasets, which hinders model generalization and consistent evaluation across different models. In this paper, we introduce the IMed-361M benchmark dataset, a significant advancement in general IMIS research. First, we collect and standardize over 6.4 million medical images and their corresponding ground truth masks from multiple data sources. Then, leveraging the strong object recognition capabilities of a vision foundational model, we automatically generated dense interactive masks for each image and ensured their quality through rigorous quality control and granularity management. Unlike previous datasets, which are limited by specific modalities or sparse annotations, IMed-361M spans 14 modalities and 204 segmentation targets, totaling 361 million masks-an average of 56 masks per image. Finally, we developed an IMIS baseline network on this dataset that supports high-quality mask generation through interactive inputs, including clicks, bounding boxes, text prompts, and their combinations. We evaluate its performance on medical image segmentation tasks from multiple perspectives, demonstrating superior accuracy and scalability compared to existing interactive segmentation models. To facilitate research on foundational models in medical computer vision, we release the IMed-361M and model at https://github.com/uni-medical/IMIS-Bench.