Robust Segmentation

Robust segmentation aims to create image segmentation models that are resilient to noise, variations in data acquisition, and adversarial attacks, ultimately improving the reliability and accuracy of automated image analysis. Current research focuses on enhancing model robustness through techniques like adversarial training, data augmentation informed by sensitivity analysis, and the incorporation of architectures such as U-Nets, transformers, and feature pyramid networks, often combined with novel loss functions or uncertainty quantification methods. These advancements are crucial for reliable applications across diverse fields, including medical imaging diagnosis, autonomous driving, and remote sensing, where accurate and dependable segmentation is paramount for effective decision-making.

Papers

December 28, 2022

CT-LungNet: A Deep Learning Framework for Precise Lung Tissue Segmentation in 3D Thoracic CT Scans
Niloufar Delfan, Hamid Abrishami Moghaddam, Mohammadreza Modaresi, Kimia Afshari, Kasra Nezamabadi, Neda Pak, Omid Ghaemi, Mohamad Forouzanfar
Deep Learning Framework Lung Segmentation 3d Ct Robust Segmentation Chest CT Lung CT

August 11, 2022

July 25, 2022

SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness
Jindong Gu, Hengshuang Zhao, Volker Tresp, Philip Torr
Adversarial Example Adversarial Training Adversarial Perturbation Robust Segmentation Efficient Adversarial Attack

July 5, 2022

Vector Quantisation for Robust Segmentation
Ainkaran Santhirasekaram, Avinash Kori, Mathias Winkler, Andrea Rockall, Ben Glocker
Segmentation Model Vector Quantization Better Robustness Robust Segmentation Vector Quantisation

July 1, 2022

Online Reflective Learning for Robust Medical Image Segmentation
Yuhao Huang, Xin Yang, Xiaoqiong Huang, Jiamin Liang, Xinrui Zhou, Cheng Chen, Haoran Dou, Xindi Hu, Yan Cao, Dong Ni
Segmentation Model Robust Segmentation Segmentation Error Robust Medical Image Segmentation

June 19, 2022

TBraTS: Trusted Brain Tumor Segmentation
Ke Zou, Xuedong Yuan, Xiaojing Shen, Meng Wang, Huazhu Fu
Brain Tumor Segmentation Segmentation Result Reliable Uncertainty Robust Segmentation

April 3, 2022

Adversarially robust segmentation models learn perceptually-aligned gradients
Pedro Sandoval-Segura
Semantic Segmentation Network Gradient Alignment Robust Segmentation

March 3, 2022

Robust Segmentation of Brain MRI in the Wild with Hierarchical CNNs and no Retraining
Benjamin Billot, Magdamo Colin, Sean E. Arnold, Sudeshna Das, Juan. E. Iglesias
Wild Challenge Brain MRI Robust Segmentation Retraining Method Neuroimaging Study

January 11, 2022

Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task
Himashi Peiris, Zhaolin Chen, Gary Egan, Mehrtash Harandi
Adversarial Learning Segmentation Task Solution Path Brain Tumor Segmentation Brain Tumor Robust Segmentation BRAT Challenge Virtual Adversarial Training Reciprocal Learning

Robust Segmentation

Papers

CT-LungNet: A Deep Learning Framework for Precise Lung Tissue Segmentation in 3D Thoracic CT Scans

Region-Based Evidential Deep Learning to Quantify Uncertainty and Improve Robustness of Brain Tumor Segmentation

TotalSegmentator: robust segmentation of 104 anatomical structures in CT images

SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness

Vector Quantisation for Robust Segmentation

Online Reflective Learning for Robust Medical Image Segmentation

TBraTS: Trusted Brain Tumor Segmentation

Adversarially robust segmentation models learn perceptually-aligned gradients

Robust Segmentation of Brain MRI in the Wild with Hierarchical CNNs and no Retraining

Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task