Pay Attention to the Atlas: Atlas-Guided Test-Time Adaptation Method for Robust 3D Medical Image Segmentation

• URL: http://arxiv.org/abs/2307.00676v2
• Date: Tue, 27 Feb 2024 13:46:11 GMT
• Title: Pay Attention to the Atlas: Atlas-Guided Test-Time Adaptation Method for Robust 3D Medical Image Segmentation
• Authors: Jingjie Guo, Weitong Zhang, Matthew Sinclair, Daniel Rueckert, Chen Chen
• Abstract summary: Convolutional neural networks (CNNs) often suffer from poor performance when tested on target data that differs from the training (source) data distribution. We propose a novel atlas-guided test-time adaptation (TTA) method for robust 3D medical image segmentation, called AdaAtlas.
• Score: 16.606821084149406
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Convolutional neural networks (CNNs) often suffer from poor performance when tested on target data that differs from the training (source) data distribution, particularly in medical imaging applications where variations in imaging protocols across different clinical sites and scanners lead to different imaging appearances. However, re-accessing source training data for unsupervised domain adaptation or labeling additional test data for model fine-tuning can be difficult due to privacy issues and high labeling costs, respectively. To solve this problem, we propose a novel atlas-guided test-time adaptation (TTA) method for robust 3D medical image segmentation, called AdaAtlas. AdaAtlas only takes one single unlabeled test sample as input and adapts the segmentation network by minimizing an atlas-based loss. Specifically, the network is adapted so that its prediction after registration is aligned with the learned atlas in the atlas space, which helps to reduce anatomical segmentation errors at test time. In addition, different from most existing TTA methods which restrict the adaptation to batch normalization blocks in the segmentation network only, we further exploit the use of channel and spatial attention blocks for improved adaptability at test time. Extensive experiments on multiple datasets from different sites show that AdaAtlas with attention blocks adapted (AdaAtlas-Attention) achieves superior performance improvements, greatly outperforming other competitive TTA methods.

Related papers

• Single Image Test-Time Adaptation for Segmentation [22.600586011303363]
  This work explores adapting segmentation models to a single unlabelled image with no other data available at test-time. In particular, this work focuses on adaptation by optimizing self-supervised losses at test-time. Our additions to the baselines result in a 3.51 and 3.28 % increase over non-adapted baselines.
  arXiv  Detail & Related papers  (2023-09-25T11:31:18Z)
• Cross-Dataset Adaptation for Instrument Classification in Cataract Surgery Videos [54.1843419649895]
  State-of-the-art models, which perform this task well on a particular dataset, perform poorly when tested on another dataset. We propose a novel end-to-end Unsupervised Domain Adaptation (UDA) method called the Barlow Adaptor. In addition, we introduce a novel loss called the Barlow Feature Alignment Loss (BFAL) which aligns features across different domains.
  arXiv  Detail & Related papers  (2023-07-31T18:14:18Z)
• Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation [35.381677272157866]
  Pulmonary nodules and masses are crucial imaging features in lung cancer screening. Despite the success of deep learning-based medical image segmentation, the robust performance on various sizes of lesions is still challenging. We propose a multi-scale neural network with scale-aware test-time adaptation to address this challenge.
  arXiv  Detail & Related papers  (2023-07-28T16:04:34Z)
• CAFA: Class-Aware Feature Alignment for Test-Time Adaptation [50.26963784271912]
  Test-time adaptation (TTA) aims to address this challenge by adapting a model to unlabeled data at test time. We propose a simple yet effective feature alignment loss, termed as Class-Aware Feature Alignment (CAFA), which simultaneously encourages a model to learn target representations in a class-discriminative manner.
  arXiv  Detail & Related papers  (2022-06-01T03:02:07Z)
• DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Images [56.72015587067494]
  We propose a novel dynamic learning rate adjustment method for test-time adaptation, called DLTTA. Our method achieves effective and fast test-time adaptation with consistent performance improvement over current state-of-the-art test-time adaptation methods.
  arXiv  Detail & Related papers  (2022-05-27T02:34:32Z)
• A Field of Experts Prior for Adapting Neural Networks at Test Time [8.244295783641396]
  Performance of convolutional neural networks (CNNs) in image analysis tasks is often marred by acquisition-related distribution shifts between training and test images. It has been proposed to tackle this problem by fine-tuning trained CNNs for each test image. We propose to carry out test-time-adaptation (TTA) by matching the feature distributions of test and training images.
  arXiv  Detail & Related papers  (2022-02-10T11:44:45Z)
• Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection [85.11649974840758]
  3D object detection networks tend to be biased towards the data they are trained on. We propose a single-frame approach for source-free, unsupervised domain adaptation of lidar-based 3D object detectors.
  arXiv  Detail & Related papers  (2021-11-30T18:42:42Z)
• Weakly-supervised Learning For Catheter Segmentation in 3D Frustum Ultrasound [74.22397862400177]
  We propose a novel Frustum ultrasound based catheter segmentation method. The proposed method achieved the state-of-the-art performance with an efficiency of 0.25 second per volume.
  arXiv  Detail & Related papers  (2020-10-19T13:56:22Z)
• Deep Mining External Imperfect Data for Chest X-ray Disease Screening [57.40329813850719]
  We argue that incorporating an external CXR dataset leads to imperfect training data, which raises the challenges. We formulate the multi-label disease classification problem as weighted independent binary tasks according to the categories. Our framework simultaneously models and tackles the domain and label discrepancies, enabling superior knowledge mining ability.
  arXiv  Detail & Related papers  (2020-06-06T06:48:40Z)
• Progressive Adversarial Semantic Segmentation [11.323677925193438]
  Deep convolutional neural networks can perform exceedingly well given full supervision. The success of such fully-supervised models for various image analysis tasks is limited to the availability of massive amounts of labeled data. We propose a novel end-to-end medical image segmentation model, namely Progressive Adrial Semantic (PASS)
  arXiv  Detail & Related papers  (2020-05-08T22:48:00Z)

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.