AtlasSeg: Atlas Prior Guided Dual-U-Net for Cortical Segmentation in Fetal Brain MRI

• URL: http://arxiv.org/abs/2411.02867v1
• Date: Tue, 05 Nov 2024 07:16:32 GMT
• Title: AtlasSeg: Atlas Prior Guided Dual-U-Net for Cortical Segmentation in Fetal Brain MRI
• Authors: Haoan Xu, Tianshu Zheng, Xinyi Xu, Yao Shen, Jiwei Sun, Cong Sun, Guangbin Wang, Dan Wu,
• Abstract summary: We introduce AtlasSeg, a dual-U-shape convolution network incorporating gestational age (GA) specific information as guidance. By providing a publicly available fetal brain atlas with segmentation label at the corresponding GA, AtlasSeg effectively extracted the contextual features of age-specific patterns in atlas branch. AtlasSeg outperformed six well-known segmentation networks in both our internal fetal brain MRI dataset and the external FeTA dataset.
• Score: 11.764796934909462
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate tissue segmentation in fetal brain MRI remains challenging due to the dynamically changing anatomical anatomy and contrast during fetal development. To enhance segmentation accuracy throughout gestation, we introduced AtlasSeg, a dual-U-shape convolution network incorporating gestational age (GA) specific information as guidance. By providing a publicly available fetal brain atlas with segmentation label at the corresponding GA, AtlasSeg effectively extracted the contextual features of age-specific patterns in atlas branch and generated tissue segmentation in segmentation branch. Multi-scale attentive atlas feature fusions were constructed in all stages during encoding and decoding, giving rise to a dual-U-shape network to assist feature flow and information interactions between two branches. AtlasSeg outperformed six well-known segmentation networks in both our internal fetal brain MRI dataset and the external FeTA dataset. Ablation experiments demonstrate the efficiency of atlas guidance and the attention mechanism. The proposed AtlasSeg demonstrated superior segmentation performance against other convolution networks with higher segmentation accuracy, and may facilitate fetal brain MRI analysis in large-scale fetal brain studies.

Related papers

• Conditional Fetal Brain Atlas Learning for Automatic Tissue Segmentation [0.8326370619658685]
  We introduce a novel deep-learning framework for generating continuous, age-specific fetal brain atlases.<n>The framework combines a direct registration model with a conditional discriminator.<n>It was trained on a curated dataset of 219 neurotypical fetal MRIs spanning from 21 to 37 weeks of gestation.
  arXiv  Detail & Related papers  (2025-08-06T15:07:39Z)
• DB-SAM: Delving into High Quality Universal Medical Image Segmentation [100.63434169944853]
  We propose a dual-branch adapted SAM framework, named DB-SAM, to bridge the gap between natural and 2D/3D medical data. Our proposed DB-SAM achieves an absolute gain of 8.8%, compared to a recent medical SAM adapter in the literature.
  arXiv  Detail & Related papers  (2024-10-05T14:36:43Z)
• Adaptive Smooth Activation for Improved Disease Diagnosis and Organ Segmentation from Radiology Scans [2.788038354941588]
  We propose a new activation function, called Adaptive Smooth Activation Unit (ASAU), tailored for optimized gradient propagation. We apply ASAU to two important and commonly used general tasks in medical image analysis: automatic disease diagnosis and organ segmentation in CT and MRI.
  arXiv  Detail & Related papers  (2023-11-29T07:16:55Z)
• Uncertainty Driven Bottleneck Attention U-net for Organ at Risk Segmentation [20.865775626533434]
  Organ at risk (OAR) segmentation in computed tomography (CT) imagery is a difficult task for automated segmentation methods. We propose a multiple decoder U-net architecture and use the segmentation disagreement between the decoders as attention to the bottleneck of the network. For accurate segmentation, we also proposed a CT intensity integrated regularization loss.
  arXiv  Detail & Related papers  (2023-03-19T23:45:32Z)
• 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)
• 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)
• CAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRI [10.127399319119911]
  We propose a novel network structure that can simultaneously generate conditional atlases and predict brain tissue segmentation. The proposed method is trained and evaluated on 253 subjects from the developing Human Connectome Project.
  arXiv  Detail & Related papers  (2022-05-17T11:23:02Z)
• An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis [1.3198175418055964]
  We report the creation of two new fetal atlases, and their use within the pipeline for atlas-based segmentation. The pipeline is also able to extract cortical and pial surfaces and compute features, such as curvature, thickness, sulcal depth, and local gyrification index.
  arXiv  Detail & Related papers  (2022-05-16T11:15:26Z)
• BAGNet: Bidirectional Aware Guidance Network for Malignant Breast lesions Segmentation [5.823080777200961]
  The bidirectional aware guidance network (BAGNet) is proposed to segment the malignant lesion from breast ultrasound images. BAGNet captures the context between global (low-level) and local (high-level) features from the input coarse saliency map. The introduction of the global feature map can reduce the interference of surrounding tissue (background) on the lesion regions.
  arXiv  Detail & Related papers  (2022-04-28T08:28:06Z)
• Category Guided Attention Network for Brain Tumor Segmentation in MRI [6.685945448824158]
  We propose a novel segmentation network named Category Guided Attention U-Net (CGA U-Net) In this model, we design a Supervised Attention Module (SAM) based on the attention mechanism, which can capture more accurate and stable long-range dependency in feature maps without introducing much computational cost. Experimental results on the BraTS 2019 datasets show that the proposed method outperformers the state-of-the-art algorithms in both segmentation performance and computational complexity.
  arXiv  Detail & Related papers  (2022-03-29T09:22:29Z)
• Factored Attention and Embedding for Unstructured-view Topic-related Ultrasound Report Generation [70.7778938191405]
  We propose a novel factored attention and embedding model (termed FAE-Gen) for the unstructured-view topic-related ultrasound report generation. The proposed FAE-Gen mainly consists of two modules, i.e., view-guided factored attention and topic-oriented factored embedding, which capture the homogeneous and heterogeneous morphological characteristic across different views.
  arXiv  Detail & Related papers  (2022-03-12T15:24:03Z)
• Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation [55.1248480381153]
  We propose OrganNet which learns a generalized organ concept from a set of annotated organ classes and then transfer this concept to unseen classes. We show that OrganNet can effectively resist the wide variations in organ morphology and produce state-of-the-art results in one-shot segmentation task.
  arXiv  Detail & Related papers  (2021-03-30T13:41:12Z)
• 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)
• DONet: Dual Objective Networks for Skin Lesion Segmentation [77.9806410198298]
  We propose a simple yet effective framework, named Dual Objective Networks (DONet), to improve the skin lesion segmentation. Our DONet adopts two symmetric decoders to produce different predictions for approaching different objectives. To address the challenge of large variety of lesion scales and shapes in dermoscopic images, we additionally propose a recurrent context encoding module (RCEM)
  arXiv  Detail & Related papers  (2020-08-19T06:02:46Z)
• 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)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)
• 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)
• Weakly-Supervised Lesion Segmentation on CT Scans using Co-Segmentation [18.58056402884405]
  Lesion segmentation on computed tomography (CT) scans is an important step for precisely monitoring changes in lesion/tumor growth. Current practices rely on an imprecise substitute called response evaluation criteria in solid tumors. This paper proposes a convolutional neural network based weakly-supervised lesion segmentation method.
  arXiv  Detail & Related papers  (2020-01-23T15:15:53Z)

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.