Medical SAM 2: Segment medical images as video via Segment Anything
  Model 2
In this paper, we introduce Medical SAM 2 (MedSAM-2), an advanced
segmentation model that utilizes the SAM 2 framework to address both 2D and 3D
medical image segmentation tasks. By adopting the philosophy of taking medical
images as videos, MedSAM-2 not only applies to 3D medical images but also
unlocks new One-prompt Segmentation capability. That allows users to provide a
prompt for just one or a specific image targeting an object, after which the
model can autonomously segment the same type of object in all subsequent
images, regardless of temporal relationships between the images. We evaluated
MedSAM-2 across a variety of medical imaging modalities, including abdominal
organs, optic discs, brain tumors, thyroid nodules, and skin lesions, comparing
it against state-of-the-art models in both traditional and interactive
segmentation settings. Our findings show that MedSAM-2 not only surpasses
existing models in performance but also exhibits superior generalization across
a range of medical image segmentation tasks. Our code will be released at:
https://github.com/MedicineToken/Medical-SAM2
66b043e987f605ac4dabcad1
Jiayuan Zhu
extracted_confirmed
2025-02-28T14:12:11.497Z
66aff1d8ccc0fb3883dd19a8
/avatars/dc9a0c622f0509c5bc9bf82d8f6ad7e3.svg
Jiayuan Zhu
user
jiayuanz3
66b043e987f605ac4dabcad2
Yunli Qi
admin_assigned
2024-08-05T08:03:37.405Z
65733dc5addd9129ff1fc477
/avatars/834df10741464101e53933692ade68a7.svg
Yunli Qi
user
yunliqi
66b043e987f605ac4dabcad3
Junde Wu
extracted_confirmed
2024-08-11T12:53:12.923Z
6317257fc92fd6fee317ff7c
/avatars/2f460a2f28562c987becb2acad8d93e7.svg
Junde Wu
user
morson
Medical SAM 2: Segment medical images as video via Segment Anything
  Model 2
In this paper, we introduce Medical SAM 2 (MedSAM-2), an advanced
segmentation model that utilizes the SAM 2 framework to address both 2D and 3D
medical image segmentation tasks. By adopting the philosophy of taking medical
images as videos, MedSAM-2 not only applies to 3D medical images but also
unlocks new One-prompt Segmentation capability. That allows users to provide a
prompt for just one or a specific image targeting an object, after which the
model can autonomously segment the same type of object in all subsequent
images, regardless of temporal relationships between the images. We evaluated
MedSAM-2 across a variety of medical imaging modalities, including abdominal
organs, optic discs, brain tumors, thyroid nodules, and skin lesions, comparing
it against state-of-the-art models in both traditional and interactive
segmentation settings. Our findings show that MedSAM-2 not only surpasses
existing models in performance but also exhibits superior generalization across
a range of medical image segmentation tasks. Our code will be released at:
https://github.com/MedicineToken/Medical-SAM2
MedSAM-2 extends the SAM 2 framework for 2D and 3D medical image segmentation, offering one-prompt segmentation across multiple images and outperforming existing models in various medical imaging tasks.
SAM 2 Medical SAM 2 MedSAM-2 video-based segmentation One-prompt Segmentation medical imaging modalities abdominal organs optic discs brain tumors thyroid nodules skin lesions interactive segmentation