Uncertainty-Aware Lung Nodule Segmentation with Multiple Annotations

• URL: http://arxiv.org/abs/2110.12372v1
• Date: Sun, 24 Oct 2021 07:19:37 GMT
• Title: Uncertainty-Aware Lung Nodule Segmentation with Multiple Annotations
• Authors: Qiuli Wang, Han Yang, Lu Shen, Mengke Zhang
• Abstract summary: This paper proposes an Uncertainty-Aware Network (UAS-Net) based on multi-branch U-Net. It can learn the valuable visual features from the regions that may cause segmentation uncertainty. It can also provide a Multi-Confidence Mask (MCM) simultaneously, pointing out regions with different segmentation uncertainty levels.
• Score: 0.8602553195689513
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Since radiologists have different training and clinical experience, they may provide various segmentation maps for a lung nodule. As a result, for a specific lung nodule, some regions have a higher chance of causing segmentation uncertainty, which brings difficulty for lung nodule segmentation with multiple annotations. To address this problem, this paper proposes an Uncertainty-Aware Segmentation Network (UAS-Net) based on multi-branch U-Net, which can learn the valuable visual features from the regions that may cause segmentation uncertainty and contribute to a better segmentation result. Meanwhile, this network can provide a Multi-Confidence Mask (MCM) simultaneously, pointing out regions with different segmentation uncertainty levels. We introduce a Feature-Aware Concatenation structure for different learning targets and let each branch have a specific learning preference. Moreover, a joint adversarial learning process is also adopted to help learn discriminative features of complex structures. Experimental results show that our method can predict the reasonable regions with higher uncertainty and improve lung nodule segmentation performance in LIDC-IDRI.

Related papers

• Ambiguous Medical Image Segmentation using Diffusion Models [60.378180265885945]
  We introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent sampling process of diffusion. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
  arXiv  Detail & Related papers  (2023-04-10T17:58:22Z)
• MESAHA-Net: Multi-Encoders based Self-Adaptive Hard Attention Network with Maximum Intensity Projections for Lung Nodule Segmentation in CT Scan [6.266053305874546]
  We propose an efficient end-to-end framework, the multi-encoder-based self-adaptive hard attention network (MESAHA-Net) for precise lung nodule segmentation in CT scans. MESAHA-Net iteratively performs slice-by-slice 2D segmentation of lung nodules, focusing on the nodule region in each slice to generate 3D segmentation of lung nodules. The proposed framework has been evaluated on the LIDC-IDRI dataset, the largest publicly available dataset for lung nodule segmentation.
  arXiv  Detail & Related papers  (2023-04-04T07:05:15Z)
• Lung Nodule Segmentation and Uncertain Region Prediction with an Uncertainty-Aware Attention Mechanism [30.298653876400003]
  Radiologists possess diverse training and clinical experiences, leading to variations in the segmentation of lung nodules. Conventional methods typically select a single annotation as the learning target or attempt to learn a latent space comprising multiple annotations. We propose an Uncertainty-Aware Attention Mechanism (UAAM) that utilizes consensus and disagreements among multiple annotations to facilitate better segmentation.
  arXiv  Detail & Related papers  (2023-03-15T07:31:55Z)
• Multi-Modal Evaluation Approach for Medical Image Segmentation [4.989480853499916]
  We propose a novel multi-modal evaluation (MME) approach to measure the effectiveness of different segmentation methods. We introduce new relevant and interpretable characteristics, including detection property, boundary alignment, uniformity, total volume, and relative volume. Our proposed approach is open-source and publicly available for use.
  arXiv  Detail & Related papers  (2023-02-08T15:31:33Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• A Deep Ensemble Learning Approach to Lung CT Segmentation for COVID-19 Severity Assessment [0.5512295869673147]
  We present a novel deep learning approach to categorical segmentation of lung CTs of COVID-19 patients. We partition the scans into healthy lung tissues, non-lung regions, and two different, yet visually similar, pathological lung tissues. The proposed framework achieves competitive results and outstanding generalization capabilities for three COVID-19 datasets.
  arXiv  Detail & Related papers  (2022-07-05T21:28:52Z)
• MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning [92.91544082745196]
  Federated learning (FL) has been widely employed for medical image analysis. FL's performance is limited for multiple sclerosis (MS) lesion segmentation tasks. We propose the first FL MS lesion segmentation framework via two effective re-weighting mechanisms.
  arXiv  Detail & Related papers  (2022-05-03T14:06:03Z)
• Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation [92.9634065964963]
  We present a new semi-supervised segmentation model, namely, conservative-radical network (CoraNet) based on our uncertainty estimation and separate self-training strategy. Compared with the current state of the art, our CoraNet has demonstrated superior performance.
  arXiv  Detail & Related papers  (2021-10-17T08:49:33Z)
• Multi-class probabilistic atlas-based whole heart segmentation method in cardiac CT and MRI [4.144197343838299]
  This article proposes a framework for multi-class whole heart segmentation employing non-rigid registration-based probabilistic atlas. We also propose a non-rigid registration pipeline utilizing a multi-resolution strategy for obtaining the highest attainable mutual information. The proposed approach exhibits an encouraging achievement, yielding a mean volume overlapping error of 14.5 % for CT scans.
  arXiv  Detail & Related papers  (2021-02-03T01:02:09Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• Towards Cross-modality Medical Image Segmentation with Online Mutual Knowledge Distillation [71.89867233426597]
  In this paper, we aim to exploit the prior knowledge learned from one modality to improve the segmentation performance on another modality. We propose a novel Mutual Knowledge Distillation scheme to thoroughly exploit the modality-shared knowledge. Experimental results on the public multi-class cardiac segmentation data, i.e., MMWHS 2017, show that our method achieves large improvements on CT segmentation.
  arXiv  Detail & Related papers  (2020-10-04T10:25:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.