Head and Neck Tumor Lesion Segmentation, Diagnosis and Prognosis : 4th Challenge, HECKTOR 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings / edited by Numan Saeed, Shahad Hardan, Salma Hassan, Mohammad Yaqub.

Format:

Book

Author/Creator:

Saeed, Numan.

Series:

Lecture Notes in Computer Science, 1611-3349 ; 16520

Language:

English

Subjects (All):

Image processing--Digital techniques.

Image processing.

Computer vision.

Machine learning.

Bioinformatics.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Image Processing.

Machine Learning.

Computational and Systems Biology.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Image Processing.

Machine Learning.

Computational and Systems Biology.

Physical Description:

1 online resource (274 pages)

Edition:

1st ed. 2026.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2026.

Summary:

This book constitutes the 4th 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2025, which was held in conjunction with the 28th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2026, on September 2025. The 15 full papers included in this book were carefully reviewed and selected from 15 submissions. They were organized in topical sections as follows: Tumor and Lymph Node Segmentation; Joint Learning: Segmentation with Prognosis and/or HPV Prediction; and Downstream Prediction from PET/CT and Clinical Data.

Contents:

Tumor and Lymph Node Segmentation.

HectoMixNet: Advancing Automated Head and Neck Tumor Segmentation with Multicenter PET/CT Data.

nnU-Net v2 for Head-and-Neck PET/CT Primary Tumor and Lymph Node Segmentation: A Simple, Strong Baseline.

The Impact of Preprocessing, Augmentation, and Architecture on Head and Neck Tumor and Lymph Node Segmentation in PET/CT: A HECKTOR 2025 Challenge Report.

Head-and-Neck PET/CT Lesion Segmentation via SSIMH and SegResNet.

Multi-Phase Mandible-Anchored Automated Segmentation of Oropharyngeal GTVs in FDG-PET/CT.

Molecular-Information-Guided Framework for Head and Neck Tumor and Lymph Node Segmentation in PET/CT Images.

A Two-Stage Coarse-to-Fine Ensembling Segmentation Framework with Multi-Channel CT Enhancement for Head and Neck Tumor and Lymph Segmentation in PET and CT Image.

Joint Learning: Segmentation with Prognosis and/or HPV Prediction.

Less is More: Efficient PET/CT Segmentation and Multimodal Prediction of Recurrence-Free Survival and HPV-status in Head and Neck Cancer.

Multi-stage Multimodal Progressive Learning for Coordinated Segmentation, Diagnosis, and Prognosis in Head and Neck Cancer.

Enhancing Survival Outcomes in Head and Neck Cancer through Joint HPV Classification and Tumor Segmentation.

Multi-Task Deep Learning for Head and Neck Cancer: Segmentation, Survival Prediction, and HPV Classification in the HECKTOR 2025 Challenge.

From Pixels to Prognosis: Multimodal Learning for Head and Neck Cancer in the HECKTOR 2025 Challenge.

A Multi-modal Deep Learning Framework for Head and Neck Tumor Segmentation and Survival Prediction.

Hierarchical Multi-modal Vision Network for Head-and-Neck Tumor Segmentation and Survival Prediction.

Downstream Prediction from PET/CT and Clinical Data.

Fully Automated Diagnoses of HPV Status using PET/CT Images and Clinical Information.

Notes:

Print version record.

ISBN:

9783032257666

OCLC:

1612732175