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

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。

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 : 2023-01-01 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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