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Breast lesion segmentation in ultrasound images with limited annotated data
arXiv - CS - Computer Vision and Pattern Recognition Pub Date : 2020-01-21 , DOI: arxiv-2001.07322
Bahareh Behboodi, Mina Amiri, Rupert Brooks, Hassan Rivaz

Ultrasound (US) is one of the most commonly used imaging modalities in both diagnosis and surgical interventions due to its low-cost, safety, and non-invasive characteristic. US image segmentation is currently a unique challenge because of the presence of speckle noise. As manual segmentation requires considerable efforts and time, the development of automatic segmentation algorithms has attracted researchers attention. Although recent methodologies based on convolutional neural networks have shown promising performances, their success relies on the availability of a large number of training data, which is prohibitively difficult for many applications. Therefore, in this study we propose the use of simulated US images and natural images as auxiliary datasets in order to pre-train our segmentation network, and then to fine-tune with limited in vivo data. We show that with as little as 19 in vivo images, fine-tuning the pre-trained network improves the dice score by 21% compared to training from scratch. We also demonstrate that if the same number of natural and simulation US images is available, pre-training on simulation data is preferable.

中文翻译:

具有有限注释数据的超声图像中的乳房病变分割

超声 (US) 由于其低成本、安全和非侵入性的特点,是诊断和手术干预中最常用的成像方式之一。由于存在斑点噪声,美国图像分割目前是一个独特的挑战。由于手动分割需要相当多的努力和时间,自动分割算法的发展引起了研究人员的关注。虽然最近基于卷积神经网络的方法已经显示出有希望的性能,但它们的成功依赖于大量训练数据的可用性,这对于许多应用来说是非常困难的。因此,在本研究中,我们建议使用模拟美国图像和自然图像作为辅助数据集,以预训练我们的分割网络,然后用有限的体内数据进行微调。我们表明,与从头开始训练相比,只需 19 张活体图像,对预训练网络进行微调就能将骰子分数提高 21%。我们还证明,如果有相同数量的自然和模拟美国图像可用,则最好对模拟数据进行预训练。
更新日期:2020-01-22
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