图1 An overview of deep learning methods on medical image segmentation 早期的医学图像分割方法往往依赖于边缘检测、模板匹配技术、统计形状模型、主动轮廓和机器学习等,虽然有大量的方法被报道并在某些情况下取得了成功,但由于特征表示和困难,图像分割仍然是计算机视觉领域中最具挑战性的课题之一,特别是从医学图像中...
2D ISIC 2018:皮肤病分割数据集(跟HAM10000同时发布),包含2594张图像与分割ground truth,本数据集的训练目标是分割病灶与背景(二分),resize到200*200,并用imagenet的均值方差归一化,训练集:验证集=8:2,每轮随机从训练集中采样10%进行验证(交叉验证); 2D CoNIC:包含六个类别,即上皮细胞、结缔组织细胞、淋巴细胞...
In this paper we introduced both automatic brain image segmentation methods to extract the Corpus Callosum (CC), the first based on SLIC using parallel implementation which gives accelerated results over classical SLIC, the second method motivated by using Deep Learning approach. Finally, we compare ...
Deep learning for medical image segmentation. arXiv preprint arXiv:1505.02000.Lai, M. (2015). Deep learning for medical image segmentation. arXiv preprint arXiv:1505.02000. 2https://github.com/neuropoly/domainadaptation 3http://cmictig.cs.ucl.ac.uk/niftyweb/program.php?p=CHALLENGE...
In this paper, we give an overview of deep learning-based approaches for multi-modal medical image segmentation task. Firstly, we introduce the general principle of deep learning and multi-modal medical image segmentation. Secondly, we present different deep learning network architectures, then ...
cov- ers state-of-the-art reviews of deep learning approaches for medical image analysis, including medical image detection/recognition, medical image segmentation, medi- cal image registration, computer aided diagnosis and disease quantification, to name some of the most important addressed problems. ...
Especially, deep learning methods need more training samples, which lead to time-consuming process. Therefore, we propose a novelty model for medical image segmentation based on deep multiscale convolutional neural network (CNN) in this article. First, we extract the region of interest from the ...
Medical image segmentation is an important tool for current clinical applications. It is the backbone of numerous clinical diagnosis methods, oncological treatments and computer-integrated surgeries. A new class of machine learning algorithm, deep learni
This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using...
指出了相关领域存在的问题并指出了未来可能改进的方向。未来的方向主要包括:federated learning, benchmark dataset collection, 以及utilizing domain subject knowledge as priors。 结论:目前最新的深度学习技术在医疗图像分析上取得了巨大的成功,保证了高准确率、高效率、鲁棒性和可测量性。但目前的技术应用依赖于大规模...