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
Erscheinungsjahr 2026
ISBN: 978-3-032-23495-7
Verlag: Springer

Buch, Englisch, Band 16447, 240 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 388 g

Reihe: Lecture Notes in Computer Science

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.

Zielgruppe

Research

Autoren/Hrsg.

Ma, Jun

Kim, Sumin

Zhou, Yuyin

Wang, Bo

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Signalverarbeitung
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung

Inhaltsverzeichnis

.- 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.