SEMI-SUPERVISED LEARNING FOR PELVIC MR IMAGE SEGMENTATION BASED ON MULTI-TASK RESIDUAL FULLY CONVOLUTIONAL NETWORKS.
SEMI-SUPERVISED LEARNING FOR PELVIC MR IMAGE SEGMENTATION BASED ON MULTI-TASK RESIDUAL FULLY CONVOLUTIONAL NETWORKS.
复制标题
基于多任务剩余完全卷积网络的骨盆MR图像分割的半监督学习。
DOI：
10.1109/isbi.2018.8363713
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Shen D
中科院分区：
文献类型：
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作者：
Feng Z;Nie D;Wang L;Shen D
从磁共振 (MR) 图像中准确分割盆腔器官在图像引导放射治疗中发挥着重要作用。然而，由于器官外观不一致和形状变化较大，这是一项具有挑战性的任务。全卷积网络（FCN）最近在医学图像分割中取得了最先进的性能，但它需要大量的标记数据进行训练，而这在实际情况下通常很难获得。为了应对这些挑战，我们提出了一种基于深度学习的半监督学习框架。具体来说，我们首先基于有限数量的标记 MRI 数据训练初始多任务残差全卷积网络（FCN）。基于最初训练的FCN，可以对那些未标记的新数据进行自动分割，并将一些合理的分割（经过手动/自动检查后）纳入训练数据中以微调网络。可以重复此步骤以逐步改进我们网络的训练，直到无法包含新数据的合理分割为止。实验结果证明了我们提出的渐进式半监督学习方式的有效性及其在准确性方面的优势。
Accurate segmentation of pelvic organs from magnetic resonance (MR) images plays an important role in image-guided radiotherapy. However, it is a challenging task due to inconsistent organ appearances and large shape variations. Fully convolutional network (FCN) has recently achieved state-of-the-art performance in medical image segmentation, but it requires a large amount of labeled data for training, which is usually difficult to obtain in real situation. To address these challenges, we propose a deep learning based semi-supervised learning framework. Specifically, we first train an initial multi-task residual fully convolutional network (FCN) based on a limited number of labeled MRI data. Based on the initially trained FCN, those unlabeled new data can be automatically segmented and some reasonable segmentations (after manual/automatic checking) can be included into the training data to fine-tune the network. This step can be repeated to progressively improve the training of our network, until no reasonable segmentations of new data can be included. Experimental results demonstrate the effectiveness of our proposed progressive semi-supervised learning fashion as well as its advantage in terms of accuracy.