title: Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline
summary: 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.
paper_authors._id: 673f0f9690d6dbf6b905a252
paper_authors.name: Junlong Cheng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-25T08:34:27.090Z
paper_authors._id: 660adf0742025e0a8bb82b30
paper_authors.avatarUrl: /avatars/d47371dc32ab04176021f6223745766a.svg
paper_authors.fullname: Cheng
paper_authors.type: user
paper_authors.user: 1Junlong
paper_authors._id: 673f0f9690d6dbf6b905a253
paper_authors.name: Bin Fu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-26T22:50:28.867Z
paper_authors._id: 66cfb43aa181ad5423168202
paper_authors.avatarUrl: /avatars/1d41772842b58a734419ca53059ae49f.svg
paper_authors.fullname: Bin Fu
paper_authors.type: user
paper_authors.user: berniefu9
paper_authors._id: 673f0f9690d6dbf6b905a254
paper_authors.name: Jin Ye
paper_authors._id: 673f0f9690d6dbf6b905a255
paper_authors.name: Guoan Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-26T22:49:34.173Z
paper_authors._id: 6409b7eb96aae649740e2920
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1678358480751-noauth.png
paper_authors.fullname: Guoan Wang
paper_authors.type: user
paper_authors.user: SolooooMan
paper_authors._id: 673f0f9690d6dbf6b905a256
paper_authors.name: Tianbin Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-25T20:39:22.117Z
paper_authors._id: 64aff95a1e39d3cd14efe6d5
paper_authors.avatarUrl: /avatars/77efc2db6462dce2561116aa3a9d5b54.svg
paper_authors.fullname: li
paper_authors.type: user
paper_authors.user: foreverbeliever
paper_authors._id: 673f0f9690d6dbf6b905a257
paper_authors.name: Haoyu Wang
paper_authors._id: 673f0f9690d6dbf6b905a258
paper_authors.name: Ruoyu Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-26T22:50:00.572Z
paper_authors._id: 65037357a450492f8427c639
paper_authors.avatarUrl: /avatars/bb2dc83c49df6804d434364eb6905bb1.svg
paper_authors.fullname: Ruoyu Li
paper_authors.type: user
paper_authors.user: RuoyuLiLLM
paper_authors._id: 673f0f9690d6dbf6b905a259
paper_authors.name: He Yao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-26T22:49:52.190Z
paper_authors._id: 64c51eda5cd9506edf39fcf1
paper_authors.avatarUrl: /avatars/00f749d18150beae23ef0cbcc009cbce.svg
paper_authors.fullname: Yao He
paper_authors.type: user
paper_authors.user: heyao
paper_authors._id: 673f0f9690d6dbf6b905a25a
paper_authors.name: Junren Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-26T22:49:46.484Z
paper_authors._id: 63d881b0635499f21644d0fa
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1675133327469-noauth.png
paper_authors.fullname: Junren Chen
paper_authors.type: user
paper_authors.user: DefTvT
paper_authors._id: 673f0f9690d6dbf6b905a25b
paper_authors.name: JingWen Li
paper_authors._id: 673f0f9690d6dbf6b905a25c
paper_authors.name: Yanzhou Su
paper_authors._id: 673f0f9690d6dbf6b905a25d
paper_authors.name: Min Zhu
paper_authors._id: 673f0f9690d6dbf6b905a25e
paper_authors.name: Junjun He
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-26T22:49:28.734Z
paper_authors._id: 66b593026cef22e6ba6adb8a
paper_authors.avatarUrl: /avatars/8a94d55b85177e84d65dd0bd537e335f.svg
paper_authors.fullname: JunjunHe
paper_authors.type: user
paper_authors.user: JunjunHe
paper_title: Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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