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Author:   Jun Ma ,  Sumin Kim ,  Yuyin Zhou ,  Bo Wang
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
ISBN:  

9783032234957

ISBN 10:   3032234956
Pages:   240
Publication Date:   10 May 2026
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Not yet available  
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Table of 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.

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