Neck Tumor Segmentation

Neck tumor segmentation aims to automatically identify and delineate tumor regions in medical images (CT, PET, MRI) of the head and neck, aiding in diagnosis, treatment planning, and prognosis prediction. Current research heavily utilizes deep learning models, including variations of U-Net, transformers, and diffusion models, often incorporating multi-modal image fusion and attention mechanisms to improve accuracy and robustness. These advancements are crucial for improving the efficiency and accuracy of cancer diagnosis and treatment, potentially leading to better patient outcomes and personalized care. Furthermore, research is exploring decentralized learning approaches to overcome data sharing limitations across medical institutions.

Papers

October 16, 2024

Gradient Map-Assisted Head and Neck Tumor Segmentation: A Pre-RT to Mid-RT Approach in MRI-Guided Radiotherapy
Jintao Ren, Kim Hochreuter, Mathis Ersted Rasmussen, Jesper Folsted Kallehauge, Stine Sofia Korreman
Constructive Approach Human Head Radiation Oncology Neck Tumor Segmentation Adaptive Radiotherapy Gross Tumor Volume

August 10, 2024

SAM-FNet: SAM-Guided Fusion Network for Laryngo-Pharyngeal Tumor Detection
Jia Wei, Yun Li, Meiyu Qiu, Hongyu Chen, Xiaomao Fan, Wenbin Lei
Segment Anything Model Tumor Detection Neck Tumor Segmentation Endoscopic Imaging

May 3, 2024

An Attention Based Pipeline for Identifying Pre-Cancer Lesions in Head and Neck Clinical Images
Abdullah Alsalemi, Anza Shakeel, Mollie Clark, Syed Ali Khurram, Shan E Ahmed Raza
Segmentation Model Human Head Early Detection Segmentation Benchmark Lesion Detection Neck Tumor Segmentation

February 27, 2024

Segment anything model for head and neck tumor segmentation with CT, PET and MRI multi-modality images
Jintao Ren, Mathis Rasmussen, Jasper Nijkamp, Jesper Grau Eriksen, Stine Korreman
Segment Anything Model PET Dog Multi Modal MRI Neck Tumor Segmentation Multi Modality Image Gross Tumor Volume

January 31, 2024

Head and Neck Tumor Segmentation from [18F]F-FDG PET/CT Images Based on 3D Diffusion Model
Yafei Dong, Kuang Gong
Diffusion Model CT Image Human Head Positron Emission Tomography 3D Diffusion Model Neck Tumor Segmentation FDG Pet

January 11, 2024

Decentralized Gossip Mutual Learning (GML) for automatic head and neck tumor segmentation
Jingyun Chen, Yading Yuan
Tumor Segmentation Human Head Global Model Mutual Learning Neck Tumor Segmentation Gossip Learning

November 17, 2023

End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks
Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan
Medical Image Variational Autoencoders Tumor Segmentation Segmentation Mask Generative Adversarial Autoencoder Architecture Hamiltonian Neural Network Neck Tumor Segmentation

September 25, 2023

Unsupervised correspondence with combined geometric learning and imaging for radiotherapy applications
Edward G. A. Henderson, Marcel van Herk, Andrew F. Green, Eliana M. Vasquez Osorio
Organ Segmentation Radiation Oncology Adaptive Normalization Geometric Learning Non Rigid Registration Distortion Metric Neck Tumor Segmentation

March 30, 2023

The impact of training dataset size and ensemble inference strategies on head and neck auto-segmentation
Edward G. A. Henderson, Marcel van Herk, Eliana M. Vasquez Osorio
Convolutional Neural Network Global Impact Human Head Ensemble Method Dataset Size Training Datasets Neck Tumor Segmentation

October 29, 2022

Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation
Abhishek Srivastava, Debesh Jha, Bulent Aydogan, Mohamed E. Abazeed, Ulas Bagci
Multi Scale Tumor Segmentation Human Head Treatment Planning Neck Tumor Segmentation Scale Fusion Gross Tumor Volume

September 22, 2022

September 12, 2022

TMSS: An End-to-End Transformer-based Multimodal Network for Segmentation and Survival Prediction
Numan Saeed, Ikboljon Sobirov, Roba Al Majzoub, Mohammad Yaqub
End to End Segmentation Based Approach Multimodal Transformer Survival Prediction Multimodal Deep Learning Multimodal Network Neck Tumor Segmentation Cancer Survival

August 29, 2022

Fluorescence molecular optomic signatures improve identification of tumors in head and neck specimens
Yao Chen, Samuel S. Streeter, Brady Hunt, Hira S. Sardar, Jason R. Gunn, Laura J. Tafe, Joseph A. Paydarfar, Brian W. Pogue, Keith D. Paulsen, Kimberley S. Samkoe
Person Identification Radiomics Feature Medical Image Data Real Tumor Tumor Sub Region Neck Tumor Segmentation Fluorescence Image Gene Signature Endogenous Fluorescence

February 16, 2022

Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks
Fereshteh Yousefirizi, Ian Janzen, Natalia Dubljevic, Yueh-En Liu, Chloe Hill, Calum MacAulay, Arman Rahmim
Loss Function Segmentation Based Approach Tumor Segmentation Cross Validation Survival Analysis 3D U Net Neck Cancer Risk Score Neck Tumor Segmentation

January 11, 2022

Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images
Vincent Andrearczyk, Valentin Oreiller, Sarah Boughdad, Catherine Chez Le Rest, Hesham Elhalawani, Mario Jreige, John O. Prior, Martin Vallières, Dimitris Visvikis, Mathieu Hatt, Adrien Depeursinge
CT Image Human Head Radiomics Feature Outcome Prediction Neck Tumor Segmentation Dual Contouring

January 5, 2022

Tackling the Class Imbalance Problem of Deep Learning Based Head and Neck Organ Segmentation
Elias Tappeiner, Martin Welk, Rainer Schubert
Deep Learning Medical Image Segmentation Segmentation Task Class Imbalance Human Head Neck Tumor Segmentation Patch Selection

January 3, 2022

A Gradient Mapping Guided Explainable Deep Neural Network for Extracapsular Extension Identification in 3D Head and Neck Cancer Computed Tomography Images
Yibin Wang, Abdur Rahman, W. Neil. Duggar, P. Russell Roberts, Toms V. Thomas, Linkan Bian, Haifeng Wang
Neural Network Lymph Node Neck Tumor Segmentation Synthetic Gradient

Neck Tumor Segmentation

Papers

Gradient Map-Assisted Head and Neck Tumor Segmentation: A Pre-RT to Mid-RT Approach in MRI-Guided Radiotherapy

SAM-FNet: SAM-Guided Fusion Network for Laryngo-Pharyngeal Tumor Detection

An Attention Based Pipeline for Identifying Pre-Cancer Lesions in Head and Neck Clinical Images

Segment anything model for head and neck tumor segmentation with CT, PET and MRI multi-modality images

Head and Neck Tumor Segmentation from [18F]F-FDG PET/CT Images Based on 3D Diffusion Model

Decentralized Gossip Mutual Learning (GML) for automatic head and neck tumor segmentation

End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks

Unsupervised correspondence with combined geometric learning and imaging for radiotherapy applications

The impact of training dataset size and ensemble inference strategies on head and neck auto-segmentation

Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation

Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers

Automated head and neck tumor segmentation from 3D PET/CT

TMSS: An End-to-End Transformer-based Multimodal Network for Segmentation and Survival Prediction

Fluorescence molecular optomic signatures improve identification of tumors in head and neck specimens

Segmentation and Risk Score Prediction of Head and Neck Cancers in PET/CT Volumes with 3D U-Net and Cox Proportional Hazard Neural Networks

Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images

Tackling the Class Imbalance Problem of Deep Learning Based Head and Neck Organ Segmentation

A Gradient Mapping Guided Explainable Deep Neural Network for Extracapsular Extension Identification in 3D Head and Neck Cancer Computed Tomography Images