Contrast-Invariant Self-supervised Segmentation for Quantitative Placental MRI

• URL: http://arxiv.org/abs/2505.24739v3
• Date: Mon, 04 Aug 2025 14:50:47 GMT
• Title: Contrast-Invariant Self-supervised Segmentation for Quantitative Placental MRI
• Authors: Xinliu Zhong, Ruiying Liu, Emily S. Nichols, Xuzhe Zhang, Andrew F. Laine, Emma G. Duerden, Yun Wang,
• Abstract summary: We propose a contrast-augmented segmentation framework that leverages complementary information across multi-echo T2*-weighted MRI.<n>Our method integrates: (i) masked autoencoding (MAE) for self-supervised pretraining on unlabeled multi-echo slices; (ii) masked pseudo-labeling (MPL) for unsupervised domain adaptation across echo times; and (iii) global-local collaboration to align fine-grained features with global anatomical context.<n>Experiments on a clinical multi-echo placental MRI dataset demonstrate that our approach generalizes effectively across echo times and outperforms both single-echo and naive fusion
• Score: 4.905880741856885
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate placental segmentation is essential for quantitative analysis of the placenta. However, this task is particularly challenging in T2*-weighted placental imaging due to: (1) weak and inconsistent boundary contrast across individual echoes; (2) the absence of manual ground truth annotations for all echo times; and (3) motion artifacts across echoes caused by fetal and maternal movement. In this work, we propose a contrast-augmented segmentation framework that leverages complementary information across multi-echo T2*-weighted MRI to learn robust, contrast-invariant representations. Our method integrates: (i) masked autoencoding (MAE) for self-supervised pretraining on unlabeled multi-echo slices; (ii) masked pseudo-labeling (MPL) for unsupervised domain adaptation across echo times; and (iii) global-local collaboration to align fine-grained features with global anatomical context. We further introduce a semantic matching loss to encourage representation consistency across echoes of the same subject. Experiments on a clinical multi-echo placental MRI dataset demonstrate that our approach generalizes effectively across echo times and outperforms both single-echo and naive fusion baselines. To our knowledge, this is the first work to systematically exploit multi-echo T2*-weighted MRI for placental segmentation.

Related papers

• HDC: Hierarchical Distillation for Multi-level Noisy Consistency in Semi-Supervised Fetal Ultrasound Segmentation [2.964206587462833]
  A novel semi-supervised segmentation framework, called HDC, is proposed incorporating adaptive consistency learning with a single-teacher architecture.<n>The framework introduces a hierarchical distillation mechanism with two objectives: Correlation Guidance Loss for aligning feature representations and Mutual Information Loss for stabilizing noisy student learning.
  arXiv  Detail & Related papers  (2025-04-14T04:52:24Z)
• Task-oriented Uncertainty Collaborative Learning for Label-Efficient Brain Tumor Segmentation [6.722672686635773]
  Multi-contrast magnetic resonance imaging (MRI) plays a vital role in brain tumor segmentation and diagnosis.<n>Existing methods still face the challenges of multi-level specificity perception across different contrasts.<n>We propose a Task-oriented Uncertainty Collaborative Learning framework for multi-contrast MRI segmentation.
  arXiv  Detail & Related papers  (2025-03-07T18:44:53Z)
• Ultrasound Nodule Segmentation Using Asymmetric Learning with Simple Clinical Annotation [25.459627476201646]
  We suggest using simple aspect ratio annotations directly from ultrasound clinical diagnoses for automated nodule segmentation. An asymmetric learning framework is developed by extending the aspect ratio annotations with two types of pseudo labels. Experiments on two clinically collected ultrasound datasets (thyroid and breast) demonstrate the superior performance of our proposed method.
  arXiv  Detail & Related papers  (2024-04-23T09:07:04Z)
• Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRI [2.7962860265843563]
  We present a framework for automated fetal vessel segmentation from 3D black blood T2w MRI and anomaly classification. We target 11 cardiac vessels and three distinct aortic arch anomalies, including double aortic arch, right aortic arch, and suspected coarctation of the aorta. Our results showcase that our proposed training strategy significantly outperforms label propagation and a network trained exclusively on propagated labels.
  arXiv  Detail & Related papers  (2023-11-13T10:54:53Z)
• 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)
• Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation [84.58210297703714]
  We propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation. We design a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting. Experiments on three medical image segmentation datasets for different tasks demonstrate the outstanding performance of our method.
  arXiv  Detail & Related papers  (2023-01-12T08:19:46Z)
• MSCDA: Multi-level Semantic-guided Contrast Improves Unsupervised Domain Adaptation for Breast MRI Segmentation in Small Datasets [5.272836235045653]
  We propose a novel Multi-level Semantic-guided Contrastive Domain Adaptation framework. Our approach incorporates self-training with contrastive learning to align feature representations between domains. In particular, we extend the contrastive loss by incorporating pixel-to-pixel, pixel-to-centroid, and centroid-to-centroid contrasts.
  arXiv  Detail & Related papers  (2023-01-04T19:16:55Z)
• Placenta Segmentation in Ultrasound Imaging: Addressing Sources of Uncertainty and Limited Field-of-View [12.271784950642344]
  We propose a multi-task learning approach that combines the classification of placental location and semantic placenta segmentation in a single convolutional neural network. Our approach can deliver whole placenta segmentation using a multi-view US acquisition pipeline consisting of three stages: multi-probe image acquisition, image fusion and image segmentation.
  arXiv  Detail & Related papers  (2022-06-29T16:18:55Z)
• 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)
• Weakly-Supervised Cross-Domain Adaptation for Endoscopic Lesions Segmentation [79.58311369297635]
  We propose a new weakly-supervised lesions transfer framework, which can explore transferable domain-invariant knowledge across different datasets. A Wasserstein quantified transferability framework is developed to highlight widerange transferable contextual dependencies. A novel self-supervised pseudo label generator is designed to equally provide confident pseudo pixel labels for both hard-to-transfer and easy-to-transfer target samples.
  arXiv  Detail & Related papers  (2020-12-08T02:26:03Z)
• PSIGAN: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation based MRI segmentation [4.573421102994323]
  We develop a new joint probabilistic segmentation and image distribution matching generative adversarial network (PSIGAN) Our UDA approach models the co-dependency between images and their segmentation as a joint probability distribution. Our method achieved an overall average DSC of 0.87 on T1w and 0.90 on T2w for the abdominal organs.
  arXiv  Detail & Related papers  (2020-07-18T16:23:02Z)
• Hybrid Attention for Automatic Segmentation of Whole Fetal Head in Prenatal Ultrasound Volumes [52.53375964591765]
  We propose the first fully-automated solution to segment the whole fetal head in US volumes. The segmentation task is firstly formulated as an end-to-end volumetric mapping under an encoder-decoder deep architecture. We then combine the segmentor with a proposed hybrid attention scheme (HAS) to select discriminative features and suppress the non-informative volumetric features.
  arXiv  Detail & Related papers  (2020-04-28T14:43:05Z)
• Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation [73.84166499988443]
  We present a novel unsupervised domain adaptation framework, named as Synergistic Image and Feature Alignment (SIFA) Our proposed SIFA conducts synergistic alignment of domains from both image and feature perspectives. Experimental results on two different tasks demonstrate that our SIFA method is effective in improving segmentation performance on unlabeled target images.
  arXiv  Detail & Related papers  (2020-02-06T13:49:47Z)

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.