Pre-trained models for a Cascaded-FCN in caffe and tensorflow that segments

Overview

Cascaded-FCN

This repository contains the pre-trained models for a Cascaded-FCN in caffe and tensorflow that segments the liver and its lesions out of axial CT images and a python wrapper for dense 3D Conditional Random Fields 3D CRFs.

This work was published in MICCAI 2016 paper (arXiv link) titled :

Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional 
Neural Networks and 3D Conditional Random Fields

Caffe

Quick Start

If you want to use our code we offer an docker image, which runs our code and has all dependencies installed including the correct caffe version. After having installed docker and nvidia docker:

sudo GPU=0 nvidia-docker run -v $(pwd):/data -P --net=host --workdir=/Cascaded-FCN -ti --privileged patrickchrist/cascadedfcn bash

And than start jupyter notebook and browse to localhost:8888

jupyter notebook

Tensorflow

Please look at Readme and Documentation at https://github.com/FelixGruen/tensorflow-u-net

Citation

If you have used these models in your research please use the following BibTeX for citation :

@Inbook{Christ2016,
title="Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields",
author="Christ, Patrick Ferdinand and Elshaer, Mohamed Ezzeldin A. and Ettlinger, Florian and Tatavarty, Sunil and Bickel, Marc and Bilic, Patrick and Rempfler, Markus and Armbruster, Marco and Hofmann, Felix and D'Anastasi, Melvin and Sommer, Wieland H. and Ahmadi, Seyed-Ahmad and Menze, Bjoern H.",
editor="Ourselin, Sebastien and Joskowicz, Leo and Sabuncu, Mert R. and Unal, Gozde and Wells, William",
bookTitle="Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II",
year="2016",
publisher="Springer International Publishing",
address="Cham",
pages="415--423",
isbn="978-3-319-46723-8",
doi="10.1007/978-3-319-46723-8_48",
url="http://dx.doi.org/10.1007/978-3-319-46723-8_48"
}
@ARTICLE{2017arXiv170205970C,
   author = {{Christ}, P.~F. and {Ettlinger}, F. and {Gr{\"u}n}, F. and {Elshaera}, M.~E.~A. and 
	{Lipkova}, J. and {Schlecht}, S. and {Ahmaddy}, F. and {Tatavarty}, S. and 
	{Bickel}, M. and {Bilic}, P. and {Rempfler}, M. and {Hofmann}, F. and 
	{Anastasi}, M.~D and {Ahmadi}, S.-A. and {Kaissis}, G. and {Holch}, J. and 
	{Sommer}, W. and {Braren}, R. and {Heinemann}, V. and {Menze}, B.},
    title = "{Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1702.05970},
 primaryClass = "cs.CV",
 keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence},
     year = 2017,
}
@inproceedings{Christ2017SurvivalNetPP,
  title={SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks},
  author={Patrick Ferdinand Christ and Florian Ettlinger and Georgios Kaissis and Sebastian Schlecht and Freba Ahmaddy and Felix Gr{\"{u}n and Alexander Valentinitsch and Seyed-Ahmad Ahmadi and Rickmer Braren and Bjoern H. Menze},
  booktitle={ISBI},
  year={2017}
}

Description

This work uses 2 cascaded UNETs,

  1. In step1, a UNET segments the liver from an axial abdominal CT slice. The segmentation output is a binary mask with bright pixels denoting the segmented object. By segmenting all slices in a volume we obtain a 3D segmentation.
  2. (Optional) We enhance the liver segmentation using 3D dense CRF (conditional random field). The resulting enhanced liver segmentation is then used further for step2.
  3. In step2 another UNET takes an enlarged liver slice and segments its lesions.

The input to both networks is 572x572 generated by applying reflection mirroring at all 4 sides of a 388x388 slice. The boundary 92 pixels are reflecting, resulting in (92+388+92)x(92+388+92) = 572x572.

An illustration of the pipeline is shown below :

Illustration of the CascadedFCN pipeline

For detailed Information have a look in our presentation

3D Conditional Random Field 3DCRF

You can find the 3D CRF at 3DCRF-python. Please follow the installation description in the Readme.

License

These models are published with unrestricted use for research and educational purposes. For commercial use, please refer to the paper authors.

Kaggle Feedback Prize - Evaluating Student Writing 15th solution

Kaggle Feedback Prize - Evaluating Student Writing 15th solution First of all, I would like to thank the excellent notebooks and discussions from http

Lingyuan Zhang 6 Mar 24, 2022
yolox_backbone is a deep-learning library and is a collection of YOLOX Backbone models.

YOLOX-Backbone yolox-backbone is a deep-learning library and is a collection of YOLOX backbone models. Install pip install yolox-backbone Load a Pret

Yonghye Kwon 21 Dec 28, 2022
Implementation of "Selection via Proxy: Efficient Data Selection for Deep Learning" from ICLR 2020.

Selection via Proxy: Efficient Data Selection for Deep Learning This repository contains a refactored implementation of "Selection via Proxy: Efficien

Stanford Future Data Systems 70 Nov 16, 2022
Official implementation for “Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior”

Unsupervised Low-Light Image Enhancement via Histogram Equalization Prior. The code will release soon. Implementation Python3 PyTorch=1.0 NVIDIA GPU+

FengZhang 34 Dec 04, 2022
PyTorch implementation for STIN

STIN This repository contains PyTorch implementation for STIN. Abstract: In single-photon LiDAR, photon-efficient imaging captures the 3D structure of

Yiweins 2 Nov 22, 2022
Production First and Production Ready End-to-End Speech Recognition Toolkit

WeNet 中文版 Discussions | Docs | Papers | Runtime (x86) | Runtime (android) | Pretrained Models We share neural Net together. The main motivation of WeN

2.7k Jan 04, 2023
scikit-learn: machine learning in Python

scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license. The project was started

scikit-learn 52.5k Jan 08, 2023
Official code for 'Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentationon Complex Urban Driving Scenes'

PEBAL This repo contains the Pytorch implementation of our paper: Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentationon Complex Urba

Yu Tian 115 Dec 29, 2022
Robustness via Cross-Domain Ensembles

Robustness via Cross-Domain Ensembles [ICCV 2021, Oral] This repository contains tools for training and evaluating: Pretrained models Demo code Traini

Visual Intelligence & Learning Lab, Swiss Federal Institute of Technology (EPFL) 27 Dec 23, 2022
A little software to generate and save Julia or Mandelbrot's Fractals.

Julia-Mandelbrot-s-Fractals A little software to generate and save Julia or Mandelbrot's Fractals. Dependencies : Python 3.7 or more. (Also possible t

Olivier 0 Jul 09, 2022
Pervasive Attention: 2D Convolutional Networks for Sequence-to-Sequence Prediction

This is a fork of Fairseq(-py) with implementations of the following models: Pervasive Attention - 2D Convolutional Neural Networks for Sequence-to-Se

Maha 490 Dec 15, 2022
SlotRefine: A Fast Non-Autoregressive Model forJoint Intent Detection and Slot Filling

SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling Reference Main paper to be cited (Di Wu et al., 2020) @article

Moore 34 Nov 03, 2022
🏎️ Accelerate training and inference of 🤗 Transformers with easy to use hardware optimization tools

Hugging Face Optimum 🤗 Optimum is an extension of 🤗 Transformers, providing a set of performance optimization tools enabling maximum efficiency to t

Hugging Face 842 Dec 30, 2022
PyTorch implementation of our ICCV 2021 paper Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer.

Unsupervised_IEPGAN This is the PyTorch implementation of our ICCV 2021 paper Intrinsic-Extrinsic Preserved GANs for Unsupervised 3D Pose Transfer. Ha

25 Oct 26, 2022
Code for the published paper : Learning to recognize rare traffic sign

Improving traffic sign recognition by active search This repo contains code for the paper : "Learning to recognise rare traffic signs" How to use this

samsja 4 Jan 05, 2023
Regularizing Generative Adversarial Networks under Limited Data (CVPR 2021)

Regularizing Generative Adversarial Networks under Limited Data [Project Page][Paper] Implementation for our GAN regularization method. The proposed r

Google 148 Nov 18, 2022
Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"

TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks This is a Python3 / Pytorch implementation of TadGAN paper. The associated

Arun 92 Dec 03, 2022
PyTorch implementation of the paper: "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", IJCAI, 2021.

PAML PyTorch implementation of the paper: "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", IJCAI, 2021. (Continuously updating ) Int

15 Nov 18, 2022
Generating Band-Limited Adversarial Surfaces Using Neural Networks

Generating Band-Limited Adversarial Surfaces Using Neural Networks This is the official repository of the technical report that was published on arXiv

3 Jul 26, 2022
C3D is a modified version of BVLC caffe to support 3D ConvNets.

C3D C3D is a modified version of BVLC caffe to support 3D convolution and pooling. The main supporting features include: Training or fine-tuning 3D Co

Meta Archive 1.1k Nov 14, 2022