Simplifying medical ultrasound : 4th International Workshop, ASMUS 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ASMUS (Workshop) (4th : 2023 : Vancouver, B.C. ; Online)最新文献

英文中文

SimNorth: A novel contrastive learning approach for clustering prenatal ultrasound images. SimNorth：一种新的产前超声图像聚类对比学习方法。

Simplifying medical ultrasound : 4th International Workshop, ASMUS 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ASMUS (Workshop) (4th : 2023 : Vancouver, B.C. ; Online)

Pub Date : 2023-10-01 DOI: 10.1007/978-3-031-44521-7_10

Juan Prieto, Chiraz Benabdelkader, Teeranan Pokaprakarn, Hina Shah, Yuri Sebastião, Qing Dan, Nariman Almnini, Arieska Nicole Diaz, Srihari Chari, Harmony Chi, Elizabeth Stringer, Jeffrey Stringer

This paper describes SimNorth, an unsupervised learning approach for classifying non-standard fetal ultrasound images. SimNorth utilizes a deep feature learning model with a novel contrastive loss function to project images with similar characteristics closer together in an embedding space while pushing apart those with different image features. We then use non-linear dimensionality reduction via t-SNE and apply standard clustering algorithms such as k-means and dbscan in 2D embedding space to identify clusters containing similar fetal structures. We compare SimNorth to other unsupervised learning techniques (such as Autoencoders, MoCo, and SimCLR) and demonstrate its superior performance based on cluster purity measures.

本文介绍了一种用于非标准胎儿超声图像分类的无监督学习方法SimNorth。SimNorth利用一种具有新型对比损失函数的深度特征学习模型，将具有相似特征的图像投影到嵌入空间中，同时将具有不同图像特征的图像分开。然后，我们通过t-SNE使用非线性降维，并在二维嵌入空间中应用k-means和dbscan等标准聚类算法来识别包含相似胎儿结构的聚类。我们将SimNorth与其他无监督学习技术（如Autoencoders， MoCo和SimCLR）进行比较，并基于聚类纯度度量证明其优越的性能。

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引用次数: 0

Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation. 噪音中的回声：利用扩散模型生成合成超声波图像，用于实际图像分割。

Pub Date : 2023-01-01 Epub Date: 2023-10-01 DOI: 10.1007/978-3-031-44521-7_4

David Stojanovski, Uxio Hermida, Pablo Lamata, Arian Beqiri, Alberto Gomez

We propose a novel pipeline for the generation of synthetic ultrasound images via Denoising Diffusion Probabilistic Models (DDPMs) guided by cardiac semantic label maps. We show that these synthetic images can serve as a viable substitute for real data in the training of deep-learning models for ultrasound image analysis tasks such as cardiac segmentation. To demonstrate the effectiveness of this approach, we generated synthetic 2D echocardiograms and trained a neural network for segmenting the left ventricle and left atrium. The performance of the network trained on exclusively synthetic images was evaluated on an unseen dataset of real images and yielded mean Dice scores of 88.6 ±4.91, 91.9 ±4.22, 85.2 ±4.83 % for left ventricular endocardium, epicardium and left atrial segmentation respectively. This represents a relative increase of 9.2, 3.3 and 13.9 % in Dice scores compared to the previous state-of-the-art. The proposed pipeline has potential for application to a wide range of other tasks across various medical imaging modalities.

我们提出了一种新方法，通过心脏语义标签图引导的去噪扩散概率模型（DDPM）生成合成超声图像。我们的研究表明，这些合成图像可以替代真实数据，用于超声图像分析任务（如心脏分割）的深度学习模型训练。为了证明这种方法的有效性，我们生成了合成的二维超声心动图，并训练了一个神经网络来分割左心室和左心房。在一个未见过的真实图像数据集上评估了在完全合成图像上训练的网络的性能，结果显示，左心室心内膜、心外膜和左心房分割的平均 Dice 分数分别为 88.6 ±4.91%、91.9 ±4.22%、85.2 ±4.83%。与之前的先进技术相比，Dice 评分分别提高了 9.2%、3.3% 和 13.9%。建议的管道有潜力应用于各种医学成像模式的其他任务。

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引用次数: 0

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