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arxiv: 2412.16085 · v1 · pith:NRS2UBYNnew · submitted 2024-12-20 · 📡 eess.IV · cs.CV

Efficient MedSAMs: Segment Anything in Medical Images on Laptop

Jun Ma , Feifei Li , Sumin Kim , Reza Asakereh , Bao-Hiep Le , Dang-Khoa Nguyen-Vu , Alexander Pfefferle , Muxin Wei
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Ruochen Gao Donghang Lyu Songxiao Yang Lennart Purucker Zdravko Marinov Marius Staring Haisheng Lu Thuy Thanh Dao Xincheng Ye Zhi Li Gianluca Brugnara Philipp Vollmuth Martha Foltyn-Dumitru Jaeyoung Cho Mustafa Ahmed Mahmutoglu Martin Bendszus Irada Pfl\"uger Aditya Rastogi Dong Ni Xin Yang Guang-Quan Zhou Kaini Wang Nicholas Heller Nikolaos Papanikolopoulos Christopher Weight Yubing Tong Jayaram K Udupa Cahill J. Patrick Yaqi Wang Yifan Zhang Francisco Contijoch Elliot McVeigh Xin Ye Shucheng He Robert Haase Thomas Pinetz Alexander Radbruch Inga Krause Erich Kobler Jian He Yucheng Tang Haichun Yang Yuankai Huo Gongning Luo Kaisar Kushibar Jandos Amankulov Dias Toleshbayev Amangeldi Mukhamejan Jan Egger Antonio Pepe Christina Gsaxner Gijs Luijten Shohei Fujita Tomohiro Kikuchi Benedikt Wiestler Jan S. Kirschke Ezequiel de la Rosa Federico Bolelli Luca Lumetti Costantino Grana Kunpeng Xie Guomin Wu Behrus Puladi Carlos Mart\'in-Isla Karim Lekadir Victor M. Campello Wei Shao Wayne Brisbane Hongxu Jiang Hao Wei Wu Yuan Shuangle Li Yuyin Zhou Bo Wang
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classification 📡 eess.IV cs.CV
keywords segmentationmedicalmodelsalgorithmsfoundationadoptionclinicalefficient
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Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation

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    ESICA delivers state-of-the-art accuracy on a five-modality 3D medical segmentation benchmark while offering a compact variant with far fewer parameters.

  2. On Efficient Variants of Segment Anything Model: A Survey

    cs.CV 2024-10 unverdicted novelty 5.0

    A survey that reviews efficient variants of the Segment Anything Model, categorizes acceleration strategies, and provides a unified hardware evaluation on benchmarks.