Skip to content

Latest commit

 

History

History
 
 

unet

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

UNet

U-Net: Convolutional Networks for Biomedical Image Segmentation

Introduction

Official Repo

Code Snippet

Abstract

There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at this http URL.

Citation

@inproceedings{ronneberger2015u,
  title={U-net: Convolutional networks for biomedical image segmentation},
  author={Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
  booktitle={International Conference on Medical image computing and computer-assisted intervention},
  pages={234--241},
  year={2015},
  organization={Springer}
}

Results and models

Cityscapes

Method Backbone Loss Crop Size Lr schd Mem (GB) Inf time (fps) mIoU mIoU(ms+flip) config download
UNet + FCN UNet-S5-D16 Cross Entropy 512x1024 160000 17.91 3.05 69.10 71.05 config model | log

DRIVE

Method Backbone Loss Image Size Crop Size Stride Lr schd Mem (GB) Inf time (fps) mDice Dice config download
UNet + FCN UNet-S5-D16 Cross Entropy 584x565 64x64 42x42 40000 0.680 - 88.38 78.67 config model | log
UNet + FCN UNet-S5-D16 Cross Entropy + Dice 584x565 64x64 42x42 40000 0.582 - 88.71 79.32 config model | log
UNet + PSPNet UNet-S5-D16 Cross Entropy 584x565 64x64 42x42 40000 0.599 - 88.35 78.62 config model | log
UNet + PSPNet UNet-S5-D16 Cross Entropy + Dice 584x565 64x64 42x42 40000 0.585 - 88.76 79.42 config model | log
UNet + DeepLabV3 UNet-S5-D16 Cross Entropy 584x565 64x64 42x42 40000 0.596 - 88.38 78.69 config model | log
UNet + DeepLabV3 UNet-S5-D16 Cross Entropy + Dice 584x565 64x64 42x42 40000 0.582 - 88.84 79.56 config model | log

STARE

Method Backbone Loss Image Size Crop Size Stride Lr schd Mem (GB) Inf time (fps) mDice Dice config download
UNet + FCN UNet-S5-D16 Cross Entropy 605x700 128x128 85x85 40000 0.968 - 89.78 81.02 config model | log
UNet + FCN UNet-S5-D16 Cross Entropy + Dice 605x700 128x128 85x85 40000 0.986 - 90.65 82.70 config model | log
UNet + PSPNet UNet-S5-D16 Cross Entropy 605x700 128x128 85x85 40000 0.982 - 89.89 81.22 config model | log
UNet + PSPNet UNet-S5-D16 Cross Entropy + Dice 605x700 128x128 85x85 40000 1.028 - 90.72 82.84 config model | log
UNet + DeepLabV3 UNet-S5-D16 Cross Entropy 605x700 128x128 85x85 40000 0.999 - 89.73 80.93 config model | log
UNet + DeepLabV3 UNet-S5-D16 Cross Entropy + Dice 605x700 128x128 85x85 40000 1.010 - 90.65 82.71 config model | log

CHASE_DB1

Method Backbone Loss Image Size Crop Size Stride Lr schd Mem (GB) Inf time (fps) mDice Dice config download
UNet + FCN UNet-S5-D16 Cross Entropy 960x999 128x128 85x85 40000 0.968 - 89.46 80.24 config model | log
UNet + FCN UNet-S5-D16 Cross Entropy + Dice 960x999 128x128 85x85 40000 0.986 - 89.52 80.40 config model | log
UNet + PSPNet UNet-S5-D16 Cross Entropy 960x999 128x128 85x85 40000 0.982 - 89.52 80.36 config model | log
UNet + PSPNet UNet-S5-D16 Cross Entropy + Dice 960x999 128x128 85x85 40000 1.028 - 89.45 80.28 config model | log
UNet + DeepLabV3 UNet-S5-D16 Cross Entropy 960x999 128x128 85x85 40000 0.999 - 89.57 80.47 config model | log
UNet + DeepLabV3 UNet-S5-D16 Cross Entropy + Dice 960x999 128x128 85x85 40000 1.010 - 89.49 80.37 config model | log

HRF

Method Backbone Loss Image Size Crop Size Stride Lr schd Mem (GB) Inf time (fps) mDice Dice config download
UNet + FCN UNet-S5-D16 Cross Entropy 2336x3504 256x256 170x170 40000 2.525 - 88.92 79.45 config model | log
UNet + FCN UNet-S5-D16 Cross Entropy + Dice 2336x3504 256x256 170x170 40000 2.623 - 89.64 80.87 config model | log
UNet + PSPNet UNet-S5-D16 Cross Entropy 2336x3504 256x256 170x170 40000 2.588 - 89.24 80.07 config model | log
UNet + PSPNet UNet-S5-D16 Cross Entropy + Dice 2336x3504 256x256 170x170 40000 2.798 - 89.69 80.96 config model | log
UNet + DeepLabV3 UNet-S5-D16 Cross Entropy 2336x3504 256x256 170x170 40000 2.604 - 89.32 80.21 config model | log
UNet + DeepLabV3 UNet-S5-D16 Cross Entropy + Dice 2336x3504 256x256 170x170 40000 2.607 - 89.56 80.71 config model | log

Note:

  • In DRIVE, STARE, CHASE_DB1, and HRF dataset, mDice is mean dice of background and vessel, while Dice is dice metric of vessel(foreground) only.