CRISP - Reliable Uncertainty Estimation for Medical Image Segmentation

• URL: http://arxiv.org/abs/2206.07664v1
• Date: Wed, 15 Jun 2022 16:56:58 GMT
• Title: CRISP - Reliable Uncertainty Estimation for Medical Image Segmentation
• Authors: Thierry Judge, Olivier Bernard, Mihaela Porumb, Agis Chartsias, Arian Beqiri, Pierre-Marc Jodoin
• Abstract summary: We propose CRISP a ContRastive Image for uncertainty Prediction method. At its core, CRISP implements a contrastive method to learn a joint latent space which encodes a distribution of valid segmentations. We use this joint latent space to compare predictions to thousands of latent vectors and provide anatomically consistent uncertainty maps.
• Score: 6.197149831796131
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate uncertainty estimation is a critical need for the medical imaging community. A variety of methods have been proposed, all direct extensions of classification uncertainty estimations techniques. The independent pixel-wise uncertainty estimates, often based on the probabilistic interpretation of neural networks, do not take into account anatomical prior knowledge and consequently provide sub-optimal results to many segmentation tasks. For this reason, we propose CRISP a ContRastive Image Segmentation for uncertainty Prediction method. At its core, CRISP implements a contrastive method to learn a joint latent space which encodes a distribution of valid segmentations and their corresponding images. We use this joint latent space to compare predictions to thousands of latent vectors and provide anatomically consistent uncertainty maps. Comprehensive studies performed on four medical image databases involving different modalities and organs underlines the superiority of our method compared to state-of-the-art approaches.

Related papers

• MedUHIP: Towards Human-In-the-Loop Medical Segmentation [5.520419627866446]
  Medical image segmentation is particularly complicated by inherent uncertainties. We propose a novel approach that integrates an textbfuncertainty-aware model with textbfhuman-in-the-loop interaction Our method showcases superior segmentation capabilities, outperforming a wide range of deterministic and uncertainty-aware models.
  arXiv  Detail & Related papers  (2024-08-03T01:06:02Z)
• QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge [93.61262892578067]
  Uncertainty in medical image segmentation tasks, especially inter-rater variability, presents a significant challenge. This variability directly impacts the development and evaluation of automated segmentation algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ)
  arXiv  Detail & Related papers  (2024-03-19T17:57:24Z)
• Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty [52.03490691733464]
  We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks. By leveraging subjective logic theory, we explicitly model probability and uncertainty for the problem of medical image segmentation. DeviS incorporates an uncertainty-aware filtering module, which utilizes the metric of uncertainty-calibrated error to filter reliable data.
  arXiv  Detail & Related papers  (2023-01-01T05:02:46Z)
• 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)
• PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training [52.895952593202054]
  We propose a new semi-supervised adversarial method called Patch Confidence Adrial Training (PCA) for medical image segmentation. PCA learns the pixel structure and context information in each patch to get enough gradient feedback, which aids the discriminator in convergent to an optimal state. Our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation.
  arXiv  Detail & Related papers  (2022-07-24T07:45:47Z)
• Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
  We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images. We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem. Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T08:41:17Z)
• Trustworthy Medical Segmentation with Uncertainty Estimation [0.7829352305480285]
  This paper introduces a new Bayesian deep learning framework for uncertainty quantification in segmentation neural networks. We evaluate the proposed framework on medical image segmentation data from Magnetic Resonances Imaging and Computed Tomography scans. Our experiments on multiple benchmark datasets demonstrate that the proposed framework is more robust to noise and adversarial attacks as compared to state-of-the-art segmentation models.
  arXiv  Detail & Related papers  (2021-11-10T22:46:05Z)
• 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)
• Uncertainty Quantification in Medical Image Segmentation with Multi-decoder U-Net [3.961279440272763]
  We exploit the medical image segmentation uncertainty by measuring segmentation performance with multiple annotations in a supervised learning manner. We propose a U-Net based architecture with multiple decoders, where the image representation is encoded with the same encoder, and segmentation referring to each annotation is estimated with multiple decoders. The proposed architecture is trained in an end-to-end manner and able to improve predictive uncertainty estimates.
  arXiv  Detail & Related papers  (2021-09-15T01:46:29Z)
• Multi-structure bone segmentation in pediatric MR images with combined regularization from shape priors and adversarial network [0.4588028371034407]
  We propose a new pre-trained regularized convolutional encoder-decoder network for the challenging task of segmenting heterogeneous pediatric magnetic resonance (MR) images. In order to obtain globally consistent predictions, we incorporate a shape priors based regularization, derived from a non-linear shape representation learnt by an auto-encoder. The proposed method performed either better or at par with previously proposed approaches for Dice, sensitivity, specificity, maximum symmetric surface distance, average symmetric surface distance, and relative absolute volume difference metrics.
  arXiv  Detail & Related papers  (2020-09-15T13:39:53Z)
• Uncertainty Quantification using Variational Inference for Biomedical Image Segmentation [0.0]
  We use an encoder decoder architecture based on variational inference techniques for segmenting brain tumour images. We evaluate our work on the publicly available BRATS dataset using Dice Similarity Coefficient (DSC) and Intersection Over Union (IOU) as the evaluation metrics.
  arXiv  Detail & Related papers  (2020-08-12T20:08:04Z)

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.