Uncertainty-Guided Lung Nodule Segmentation with Feature-Aware Attention

Published in

International Conference on Medical Image Compu...(2021)

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TL;DR

An Uncertainty-Guided Segmentation Network (UGS-Net) is proposed, which learns the rich visual features from the regions that may cause segmentation uncertainty and contributes to a better segmentation result.

Abstract

. Since radiologists have different training and clinical experiences, they may provide various segmentation annotations for a lung nodule. Conventional studies choose a single annotation as the learning target by default, but they waste valuable information of consensus or disagreements ingrained in the multiple annotations. This paper proposes an Uncertainty-Guided Segmentation Network (UGS-Net), which learns the rich visual features from the regions that may cause segmentation uncertainty and contributes to a better segmentation result. With an Uncertainty-Aware Module, this network can provide a Multi-Conﬁdence Mask (MCM), pointing out regions with different segmentation uncertainty levels. Moreover, this paper introduces a Feature-Aware Attention Module to enhance the learning of the nodule boundary and density differences. Experimental results show that our method can predict the nodule regions with different uncertainty levels and achieve superior performance in LIDC-IDRI dataset.

Authors

Han Yang

1 papers

Lu Shen

1 papers

Mengke Zhang

1 papers

References30 items

A Probabilistic Model for Segmentation of Ambiguous 3D Lung Nodule

Realistic Lung Nodule Synthesis With Multi-Target Co-Guided Adversarial Mechanism

MTGAN: Mask and Texture-driven Generative Adversarial Network for Lung Nodule Segmentation

nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?

Uncertainty-Guided Lung Nodule Segmentation with Feature-Aware Attention

Published in

International Conference on Medical Image Compu...(2021)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

Han Yang

1 papers

Lu Shen

1 papers

Mengke Zhang

1 papers

References30 items

A Probabilistic Model for Segmentation of Ambiguous 3D Lung Nodule

Realistic Lung Nodule Synthesis With Multi-Target Co-Guided Adversarial Mechanism

MTGAN: Mask and Texture-driven Generative Adversarial Network for Lung Nodule Segmentation

nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?

CoLe-CNN: Context-learning convolutional neural network with adaptive loss function for lung nodule segmentation

Channel-Attention Dense U-Net for Multichannel Speech Enhancement

Lung Nodule Segmentation Based on Convolutional Neural Networks Using Multi-orientation and Patchwise Mechanisms

NoduleNet: Decoupled False Positive Reductionfor Pulmonary Nodule Detection and Segmentation

Supervised Uncertainty Quantification for Segmentation with Multiple Annotations

A cascaded dual-pathway residual network for lung nodule segmentation in CT images.

A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities

Dual-branch residual network for lung nodule segmentation

Automated pulmonary nodule detection in CT images using deep convolutional neural networks

A Modified U-Net Convolutional Network Featuring a Nearest-neighbor Re-sampling-based Elastic-Transformation for Brain Tissue Characterization and Segmentation

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

A Probabilistic U-Net for Segmentation of Ambiguous Images

Attention U-Net: Learning Where to Look for the Pancreas

Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification

Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks

Attention is All you Need

Gabor Convolutional Networks

Hessian based approaches for 3D lung nodule segmentation

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

SGDR: Stochastic Gradient Descent with Warm Restarts

U-Net: Convolutional Networks for Biomedical Image Segmentation

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.

Stratified learning of local anatomical context for lung nodules in CT images

A Threshold Selection Method from Gray-Level Histograms

Field of Study

Computer ScienceEngineering

Venue Information

Name

International Conference on Medical Image Computing and Computer-Assisted Intervention

Type

conference

URL

http://www.miccai.org/

Alternate Names

Medical Image Computing and Computer-Assisted Intervention
MICCAI
Med Image Comput Comput Interv
Int Conf Med Image Comput Comput Interv