This is the source code of the 1st place solution for segmentation task (with Dice 90.32%) in 2021 CCF BDCI challenge.

Overview

1st place solution in CCF BDCI 2021 ULSEG challenge

This is the source code of the 1st place solution for ultrasound image angioma segmentation task (with Dice 90.32%) in 2021 CCF BDCI challenge.

[Challenge leaderboard πŸ† ]

1 Pipeline of our solution

Our solution includes data pre-processing, network training, ensemble inference and data post-processing.

drawing

Ultrasound images of hemangioma segmentation framework

1.1 Data pre-processing

To improve our performance on the leaderboard, 5-fold cross validation is used to evaluate the performance of our proposed method. In our opinion, it is necessary to keep the size distribution of tumor in the training and validation sets. We calculate the tumor area for each image and categorize the tumor size into classes: 1) less than 3200 pixels, 2) less than 7200 pixels and greater than 3200 pixels, and 3) greater than 7200 pixels. These two thresholds, 3200 pixels and 7200 pixels, are close to the tertiles. We divide images in each size grade group into 5 folds and combined different grades of single fold into new single fold. This strategy ensured that final 5 folds had similar size distribution.

drawing

Tumors of different sizes

1.2 Network training

Due to the small size of the training set, for this competition, we chose a lightweight network structure: Linknet with efficientnet-B6 encoder. Following methods are performed in data augmentation (DA): 1) horizontal flipping, 2) vertical flipping, 3) random cropping, 4) random affine transformation, 5) random scaling, 6) random translation, 7) random rotation, and 8) random shearing transformation. In addition, one of the following methods was randomly selected for enhanced data augmentation (EDA): 1) sharpening, 2) local distortion, 3) adjustment of contrast, 4) blurring (Gaussian, mean, median), 5) addition of Gaussian noise, and 6) erasing.

1.3 Ensemble inference

We ensemble five models (five folds) and do test time augmentation (TTA) for each model. TTA generally improves the generalization ability of the segmentation model. In our framework, the TTA includes vertical flipping, horizontal flipping, and rotation of 180 degrees for the segmentation task.

1.4 Data post-processing

We post-processe the obtained binary mask by removing small isolated points (RSIP) and edge median filtering (EMF) . The edge part of our predicted tumor is not smooth enough, which is not quite in line with the manual annotation of the physician, so we adopt a small trick, i.e., we do a median filtering specifically for the edge part, and the experimental results show that this can improve the accuracy of tumor segmentation.

2 Segmentation results on 2021 CCF BDCI dataset

We test our method on 2021 CCD BDCI dataset (215 for training and 107 for testing). The segmentation results of 5-fold CV based on "Linknet with efficientnet-B6 encoder" are as following:

fold Linknet Unet Att-Unet DeeplabV3+ Efficient-b5 Efficient-b6 Resnet-34 DA EDA TTA RSIP EMF Dice (%)
1 √ 85.06
1 √ √ 84.48
1 √ √ 84.72
1 √ √ 84.93
1 √ √ 86.52
1 √ √ 86.18
1 √ √ 86.91
1 √ √ √ 87.38
1 √ √ √ 88.36
1 √ √ √ √ 89.05
1 √ √ √ √ √ 89.20
1 √ √ √ √ √ √ 89.52
E √ √ √ √ √ √ 90.32

3 How to run this code?

Here, we split the whole process into 5 steps so that you can easily replicate our results or perform the whole pipeline on your private custom dataset.

  • step0, preparation of environment
  • step1, run the script preprocess.py to perform the preprocessing
  • step2, run the script train.py to train our model
  • step3, run the script inference.py to inference the test data.
  • step4, run the script postprocess.py to perform the preprocessing.

You should prepare your data in the format of 2021 CCF BDCI dataset, this is very simple, you only need to prepare: two folders store png format images and masks respectively. You can download them from [Homepage].

The complete file structure is as follows:

  |--- CCF-BDCI-2021-ULSEG-Rank1st
      |--- segmentation_models_pytorch_4TorchLessThan120
          |--- ...
          |--- ...
      |--- saved_model
          |--- pred
          |--- weights
      |--- best_model
          |--- best_model1.pth
          |--- ...
          |--- best_model5.pth
      |--- train_data
          |--- img
          |--- label
          |--- train.csv
      |--- test_data
          |--- img
          |--- predict
      |--- dataset.py
      |--- inference.py
      |--- losses.py
      |--- metrics.py
      |--- ploting.py
      |--- preprocess.py
      |--- postprocess.py
      |--- util.py
      |--- train.py
      |--- visualization.py
      |--- requirement.txt

3.1 Step0 preparation of environment

We have tested our code in following environment:

For installing these, run the following code:

pip install -r requirements.txt

3.2 Step1 preprocessing

In step1, you should run the script and train.csv can be generated under train_data fold:

python preprocess.py \
--image_path="./train_data/label" \
--csv_path="./train_data/train.csv"

3.3 Step2 training

With the csv file train.csv, you can directly perform K-fold cross validation (default is 5-fold), and the script uses a fixed random seed to ensure that the K-fold cv of each experiment is repeatable. Run the following code:

python train.py \
--input_channel=1 \
--output_class=1 \
--image_resolution=256 \
--epochs=100 \
--num_workers=2 \
--device=0 \
--batch_size=8 \
--backbone="efficientnet-b6" \
--network="Linknet" \
--initial_learning_rate=1e-7 \
--t_max=110 \
--folds=5 \
--k_th_fold=1 \
--fold_file_list="./train_data/train.csv" \
--train_dataset_path="./train_data/img" \
--train_gt_dataset_path="./train_data/label" \
--saved_model_path="./saved_model" \
--visualize_of_data_aug_path="./saved_model/pred" \
--weights_path="./saved_model/weights" \
--weights="./saved_model/weights/best_model.pth" 

By specifying the parameter k_th_fold from 1 to folds and running repeatedly, you can complete the training of all K folds. After each fold training, you need to copy the .pth file from the weights path to the best_model folder.

3.4 Step3 inference (test)

Before running the script, make sure that you have generated five models and saved them in the best_model folder. Run the following code:

python inference.py \
--input_channel=1 \
--output_class=1 \
--image_resolution=256 \
--device=0 \
--backbone="efficientnet-b6" \
--network="Linknet" \
--weights1="./saved_model/weights/best_model1.pth" \
--weights2="./saved_model/weights/best_model2.pth" \
--weights3="./saved_model/weights/best_model3.pth" \
--weights4="./saved_model/weights/best_model4.pth" \
--weights5="./saved_model/weights/best_model5.pth" \
--test_path="./test_data/img" \
--saved_path="./test_data/predict" 

The results of the model inference will be saved in the predict folder.

3.5 Step4 postprocessing

Run the following code:

python postprocess.py \
--image_path="./test_data/predict" \
--threshood=50 \
--kernel=20 

Alternatively, if you want to observe the overlap between the predicted result and the original image, we also provide a visualization script visualization.py. Modify the image path in the code and run the script directly.

drawing

Visualization of tumor margins

4 Acknowledgement

  • Thanks to the organizers of the 2021 CCF BDCI challenge.
  • Thanks to the 2020 MICCCAI TNSCUI TOP 1 for making the code public.
  • Thanks to qubvel, the author of smg and ttach, all network and TTA used in this code come from his implement.
Owner
Chenxu Peng
Data Science, Deep Learning
Chenxu Peng
KGDet: Keypoint-Guided Fashion Detection (AAAI 2021)

KGDet: Keypoint-Guided Fashion Detection (AAAI 2021) This is an official implementation of the AAAI-2021 paper "KGDet: Keypoint-Guided Fashion Detecti

Qian Shenhan 35 Dec 29, 2022
AI Virtual Calculator: This is a simple virtual calculator based on Artificial intelligence.

AI Virtual Calculator: This is a simple virtual calculator that works with gestures using OpenCV. We will use our hand in the air to click on the calc

Md. Rakibul Islam 1 Jan 13, 2022
Official repo for QHackβ€”the quantum machine learning hackathon

Note: This repository has been frozen while we consider the submissions for the QHack Open Hackathon. We hope you enjoyed the event! Welcome to QHack,

Xanadu 118 Jan 05, 2023
FindFunc is an IDA PRO plugin to find code functions that contain a certain assembly or byte pattern, reference a certain name or string, or conform to various other constraints.

FindFunc: Advanced Filtering/Finding of Functions in IDA Pro FindFunc is an IDA Pro plugin to find code functions that contain a certain assembly or b

213 Dec 17, 2022
DyNet: The Dynamic Neural Network Toolkit

The Dynamic Neural Network Toolkit General Installation C++ Python Getting Started Citing Releases and Contributing General DyNet is a neural network

Chris Dyer's lab @ LTI/CMU 3.3k Jan 06, 2023
A simple interface for editing natural photos with generative neural networks.

Neural Photo Editor A simple interface for editing natural photos with generative neural networks. This repository contains code for the paper "Neural

Andy Brock 2.1k Dec 29, 2022
Download files from DSpace systems (because for some reason DSpace won't let you)

DSpaceDL A tool for downloading files from DSpace items. For some reason, DSpace systems have a dogshit UI, and Universities absolutely LOOOVE to use

Soumitra Shewale 5 Dec 01, 2022
Deep Q-Learning Network in pytorch (not actively maintained)

pytoch-dqn This project is pytorch implementation of Human-level control through deep reinforcement learning and I also plan to implement the followin

Hung-Tu Chen 342 Jan 01, 2023
Visualize Camera's Pose Using Extrinsic Parameter by Plotting Pyramid Model on 3D Space

extrinsic2pyramid Visualize Camera's Pose Using Extrinsic Parameter by Plotting Pyramid Model on 3D Space Intro A very simple and straightforward modu

JEONG HYEONJIN 106 Dec 28, 2022
2D Time independent Schrodinger equation solver for arbitrary shape of well

Schrodinger Well Python Python solver for timeless Schrodinger equation for well with arbitrary shape https://imgur.com/a/jlhK7OZ Pictures of circular

WeightAn 24 Nov 18, 2022
Code for the ICCV'21 paper "Context-aware Scene Graph Generation with Seq2Seq Transformers"

ICCV'21 Context-aware Scene Graph Generation with Seq2Seq Transformers Authors: Yichao Lu*, Himanshu Rai*, Cheng Chang*, Boris Knyazev†, Guangwei Yu,

Layer6 Labs 37 Dec 18, 2022
Learning infinite-resolution image processing with GAN and RL from unpaired image datasets, using a differentiable photo editing model.

Exposure: A White-Box Photo Post-Processing Framework ACM Transactions on Graphics (presented at SIGGRAPH 2018) Yuanming Hu1,2, Hao He1,2, Chenxi Xu1,

Yuanming Hu 719 Dec 29, 2022
Doosan robotic arm, simulation, control, visualization in Gazebo and ROS2 for Reinforcement Learning.

Robotic Arm Simulation in ROS2 and Gazebo General Overview This repository includes: First, how to simulate a 6DoF Robotic Arm from scratch using GAZE

David Valencia 12 Jan 02, 2023
Script that receives an Image (original) and a set of images to be used as "pixels" in reconstruction of the Original image using the set of images as "pixels"

picinpics Script that receives an Image (original) and a set of images to be used as "pixels" in reconstruction of the Original image using the set of

RodrigoCMoraes 1 Oct 24, 2021
Demonstrates how to divide a DL model into multiple IR model files (division) and introduce a simplest way to implement a custom layer works with OpenVINO IR models.

Demonstration of OpenVINO techniques - Model-division and a simplest-way to support custom layers Description: Model Optimizer in Intel(r) OpenVINO(tm

Yasunori Shimura 12 Nov 09, 2022
Code of TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation

TVT Code of TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation Datasets: Digit: MNIST, SVHN, USPS Object: Office, Office-Home, Vi

37 Dec 15, 2022
MAg: a simple learning-based patient-level aggregation method for detecting microsatellite instability from whole-slide images

MAg Paper Abstract File structure Dataset prepare Data description How to use MAg? Why not try the MAg_lib! Trained models Experiment and results Some

Calvin Pang 3 Apr 08, 2022
Codes for CIKM'21 paper 'Self-Supervised Graph Co-Training for Session-based Recommendation'.

COTREC Codes for CIKM'21 paper 'Self-Supervised Graph Co-Training for Session-based Recommendation'. Requirements: Python 3.7, Pytorch 1.6.0 Best Hype

Xin Xia 42 Dec 09, 2022
Easy and Efficient Object Detector

EOD Easy and Efficient Object Detector EOD (Easy and Efficient Object Detection) is a general object detection model production framework. It aim on p

381 Jan 01, 2023
Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations, CVPR 2019 (Oral)

Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations The code of: Weakly Supervised Learning of Instance Segmentation with I

Jiwoon Ahn 472 Dec 29, 2022