Foundation models for 3D biomedical image segmentation CVPR 2025 Challenge, MedSegFM 2025, held in conjunction with CVPR 2025, Nashville, TN, USA, June 11-15, 2025, Proceedings Jun Ma, Sumin Kim, Yuyin Zhou, Bo Wang, editors

Format:

Book

Conference/Event

Contributor:

Ma, Jun, editor.

Kim, Sumin, editor.

Zhou, Yuyin, editor.

Wang, Bo (Artificial intelligence scientist), editor.

Conference Name:

MedSegFM (Challenge) (2025 : Nashville, Tenn.)

IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2025 : Nashville, Tenn.)

IEEE Computer Society Conference on Computer Vision and Pattern Recognition http://viaf.org/viaf/131389625

Series:

Lecture notes in computer science ; 1611-3349 16447

Lecture notes in computer science 1611-3349 16447

Language:

English

Subjects (All):

Diagnostic imaging--Data processing--Congresses.

Diagnostic imaging.

Diagnostic imaging--Digital techniques--Congresses.

Physical Description:

1 online resource

Other Title:

MedSegFM 2025

Place of Publication:

Cham Springer 2026

Summary:

This book constitutes the proceedings of the CVPR 2025 Challenge on Foundation Models for 3D Biomedical Image Segmentation, MedSegFM 2025, held in Nashville, TN, USA, during June 11-15, 2025. The 13 full papers included in this book were carefully reviewed and selected from 19 submissions. This conference provides state-of-the-art algorithms for biomedical image segmentation foundation models

Contents:

Exploring Foundation Model Adaptations for 3D Medical Imaging: Prompt-Based Segmentation with xLSTM network.

ENSAM: an efficient foundation model for interactive segmentation of 3D medical images.

GAMT: A Geometry-Aware, Multi-View, Training-free Segmentation Framework for Foundation Models in Medical Imaging.

Five Models for Five Modalities: Open-Vocabulary Segmentation in Medical Imaging.

Medal S: Spatio-Textual Prompt Model for Medical Segmentation.

From Single-Round to Sequential: Building Stateful Interactive Medical Image Segmentation with SegVol and GRU Corrector.

BiomedParse-V : Scaling Foundation Model for Universal Text-guided Volumetric Biomedical Image Segmentation.

Enhancing a 3D Foundation Model with Gaussian Sampling for Interactive Biomedical Image Segmentation.

Dynamic Prompt Generation for Interactive 3D Medical Image Segmentation Training.

iMedSTAM: Interactive Segmentation and Tracking Anything in 3D Medical Images and Videos.

Text3DSAM: Text-Guided 3D Medical Image Segmentation Using SAM-Inspired Architecture.

Rethinking RoI Strategy in Interactive 3D Segmentation for Medical Images.

Intensity-Based Prompt Generation for Multi-Modality 3D Medical Image Segmentation

Notes:

Includes author index

Online resource; title from PDF title page (SpringerLink, viewed June 17, 2026)

Other Format:

Print version Ma, Jun Foundation Models for 3D Biomedical Image Segmentation

ISBN:

9783032234964

3032234964

OCLC:

1596699933

Access Restriction:

Restricted for use by site license