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(MICCAI 2015) U-net:Convolutional networks for biomedical image segmentation

Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[C]//International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015: 234-241.

该篇论文

  • 在FCN基础上提出U-Net结构 (Figure 1).
  • 提出医疗影像data augmentation.

结合两者能够trained end-to-end from very few images and outperforms sliding-window CNN.



Localization: a class label is supposed to be assigned to each pixel.

1. Sliding-window drawbacks


  • Network需要对每个patch单独处理,重叠的patch产生大量冗余,因此非常
  • Tradeoff between localization accuracy and the use of context. Large patches需要更多pooling层,导致localization accuracy下降,而small patches allow network see only little context.

2. Overlap-tile Strategy (Figure 2)


  • 该策略支持任意大小图片的无缝分割(seamless segmentation),蓝色区域为输入patch,黄色区域为输出patch(输入图片进行镜像处理).


3. Data Augmentation


  • Shift and rotation invariance.
  • Deformations and gray value invariance.
  • Elastic deformation非常重要,能够有效模拟组织(tissue)最常见的形变方式。

4. Touching Object Challenge (Figure 3)


  • 分离同种类型接触的细胞。
  • Propose the use of a weighted loss, where the separating background labels between touching cells obtain a large weight in the loss function.


  • 预先计算ground-truth的weight map, to force the network to learn the small separation borders that we introduce between touching cells.
  • d1,d2: the distance to the border of the nearest and second nearest cell.
  • wc: balance the class frequencies.




5. Initialization


  • Ideally the initial weights should be adapted such that each feature map in the network has approximately unit variance.
  • 论文采用Gaussian 方差srqt(2/$N$), $N$为输入Node数。例如3x3 Conv层64 kernels, 则$N$ = 9 * 64 = 576.

6. Experiment Results