Domain Generalization for Endoscopic Image Segmentation by Disentangling Style-Content Information and SuperPixel Consistency

• URL: http://arxiv.org/abs/2409.12450v1
• Date: Thu, 19 Sep 2024 04:10:04 GMT
• Title: Domain Generalization for Endoscopic Image Segmentation by Disentangling Style-Content Information and SuperPixel Consistency
• Authors: Mansoor Ali Teevno, Rafael Martinez-Garcia-Pena, Gilberto Ochoa-Ruiz, Sharib Ali,
• Abstract summary: We propose an approach for style-content disentanglement using instance normalization and instance selective whitening (ISW) for improved domain generalization. We evaluate our approach on two datasets: EndoUDA Barrett's Esophagus and EndoUDA polyps, and compare its performance with three state-of-the-art (SOTA) methods.
• Score: 1.4991956341367338
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Frequent monitoring is necessary to stratify individuals based on their likelihood of developing gastrointestinal (GI) cancer precursors. In clinical practice, white-light imaging (WLI) and complementary modalities such as narrow-band imaging (NBI) and fluorescence imaging are used to assess risk areas. However, conventional deep learning (DL) models show degraded performance due to the domain gap when a model is trained on one modality and tested on a different one. In our earlier approach, we used a superpixel-based method referred to as "SUPRA" to effectively learn domain-invariant information using color and space distances to generate groups of pixels. One of the main limitations of this earlier work is that the aggregation does not exploit structural information, making it suboptimal for segmentation tasks, especially for polyps and heterogeneous color distributions. Therefore, in this work, we propose an approach for style-content disentanglement using instance normalization and instance selective whitening (ISW) for improved domain generalization when combined with SUPRA. We evaluate our approach on two datasets: EndoUDA Barrett's Esophagus and EndoUDA polyps, and compare its performance with three state-of-the-art (SOTA) methods. Our findings demonstrate a notable enhancement in performance compared to both baseline and SOTA methods across the target domain data. Specifically, our approach exhibited improvements of 14%, 10%, 8%, and 18% over the baseline and three SOTA methods on the polyp dataset. Additionally, it surpassed the second-best method (EndoUDA) on the Barrett's Esophagus dataset by nearly 2%.

Related papers

• Local Lesion Generation is Effective for Capsule Endoscopy Image Data Augmentation in a Limited Data Setting [0.0]
  We propose and evaluate two local lesion generation approaches to address the challenge of augmenting small medical image datasets. The first approach employs the Poisson Image Editing algorithm, a classical image processing technique, to create realistic image composites. The second approach introduces a novel generative method, leveraging a fine-tuned Image Inpainting GAN to synthesize realistic lesions.
  arXiv  Detail & Related papers  (2024-11-05T13:44:25Z)
• Tackling domain generalization for out-of-distribution endoscopic imaging [1.6377635288143584]
  We exploit both style and content information in images to preserve robust and generalizable feature representations. Our proposed method shows a 13.7% improvement over the baseline DeepLabv3+ and nearly an 8% improvement over recent state-of-the-art (SOTA) methods for the target (different modality) set of the EndoUDA polyp dataset.
  arXiv  Detail & Related papers  (2024-10-18T18:45:13Z)
• Self-supervised Vision Transformer are Scalable Generative Models for Domain Generalization [0.13108652488669734]
  We propose a novel generative method for domain generalization in histopathology images. Our method employs a generative, self-supervised Vision Transformer to dynamically extract characteristics of image patches. Experiments conducted on two distinct histopathology datasets demonstrate the effectiveness of our proposed approach.
  arXiv  Detail & Related papers  (2024-07-03T08:20:27Z)
• DARC: Distribution-Aware Re-Coloring Model for Generalizable Nucleus Segmentation [68.43628183890007]
  We argue that domain gaps can also be caused by different foreground (nucleus)-background ratios. First, we introduce a re-coloring method that relieves dramatic image color variations between different domains. Second, we propose a new instance normalization method that is robust to the variation in the foreground-background ratios.
  arXiv  Detail & Related papers  (2023-09-01T01:01:13Z)
• Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective [51.70661197256033]
  We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation. We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks. We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
  arXiv  Detail & Related papers  (2023-02-03T13:50:25Z)
• Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation [48.638327652506284]
  Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. We present a novel approach, the affinity feature strengthening network (AFN), which jointly models geometry and refines pixel-wise segmentation features using a contrast-insensitive, multiscale affinity approach.
  arXiv  Detail & Related papers  (2022-11-12T05:39:17Z)
• SUPRA: Superpixel Guided Loss for Improved Multi-modal Segmentation in Endoscopy [1.1470070927586016]
  Domain shift is a well-known problem in the medical imaging community. In this paper, we explore the domain generalisation technique to enable DL methods to be used in such scenarios. We show that our method yields an improvement of nearly 20% in the target domain set compared to the baseline.
  arXiv  Detail & Related papers  (2022-11-09T03:13:59Z)
• AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation [1.0452185327816181]
  We propose a data manipulation based domain generalization method, called Automated Augmentation for Domain Generalization (AADG) Our AADG framework can effectively sample data augmentation policies that generate novel domains. Our proposed AADG exhibits state-of-the-art generalization performance and outperforms existing approaches.
  arXiv  Detail & Related papers  (2022-07-27T02:26:01Z)
• Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation [64.59521853145368]
  Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features.
  arXiv  Detail & Related papers  (2021-09-08T07:56:51Z)
• Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation [61.01704175938995]
  In this paper, we propose a novel Bidirectional Global-to-Local (BiGL) adaptation framework under a UDA scheme. Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor. The proposed method outperforms several state-of-the-art unsupervised domain adaptation methods by a large margin.
  arXiv  Detail & Related papers  (2021-05-17T10:11:45Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

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.