Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline
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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Junlong Cheng
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Cheng
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Bin Fu
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Bin Fu
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Jin Ye
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Guoan Wang
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Guoan Wang
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SolooooMan
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Tianbin Li
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li
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Haoyu Wang
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Ruoyu Li
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Ruoyu Li
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RuoyuLiLLM
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He Yao
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Yao He
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Junren Chen
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2024-11-26T22:49:46.484Z
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Junren Chen
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JingWen Li
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Yanzhou Su
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Min Zhu
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Junjun He
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2024-11-26T22:49:28.734Z
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JunjunHe
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JunjunHe
Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline
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.
The IMed-361M benchmark dataset, featuring over 6.4 million medical images and 361 million masks, enhances interactive medical image segmentation through a foundation model and diverse, high-density annotations.
interactive medical image segmentation IMed-361M vision foundational model automatic mask generation quality control interactive inputs clicks bounding boxes text prompts medical image segmentation high-quality mask generation scalability