Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline

• URL: http://arxiv.org/abs/2411.12814v2
• Date: Mon, 25 Nov 2024 03:53:16 GMT
• Title: Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline
• Authors: 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,
• Abstract summary: The IMed-361M benchmark dataset is a significant advancement in general IMIS research. We collect and standardize over 6.4 million medical images and their corresponding ground truth masks from multiple data sources. We developed an IMIS baseline network on this dataset that supports high-quality mask generation through interactive inputs.
• Score: 16.70515066552565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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