Automated Nailfold Capillary Analysis

This is a project for nailfold capillary automated analysis. The paper link: A Comprehensive Dataset and Automated Pipeline for Nailfold Capillary Analysis. Nailfold capillaroscopy stands as a traditional and classical method for health condition assessment. However, manually employing this method poses challenges as the process of human measurement is not only time-consuming but also hindered by subjective criteria. In this research, we are pioneering the construction of a large dataset comprising 321 capillaroscopy images, 219 videos, and 68 clinic reports. This dataset includes annotations such as segmentations and keypoints from experts, forming a crucial resource for training deep-learning models. Leveraging our dataset, we introduce an end-to-end nailfold capillary analysis pipeline capable of automatically detecting and measuring a comprehensive set of morphological and dynamic features. The experiments demonstrate that our automated analysis algorithms achieve remarkable accuracy, which holds promise for quantitative medical research and pervasive computing in human health. We plan to open-source our datasets and code soon to facilitate further study.

🔥 Updates

[2024/2] Contact Email is updated. Please contact tjk24@mails.tsinghua.edu.cn for the application.
[2024/2] Citation BibTex and Data Release Agreement are updated.
[2023/11] Code is updated.

🔍 Dataset

Nailfold capillary images:

Nailfold capillary video:

Data Distribution

Data Structure

ANFC_THU
├── ANFC_THU_data
 ├── ANFC_THU_keypoint
  ├── SubjectID_PicID.jpg   #raw data
  ├── SubjectID_PicID.json  #label
 ├── ANFC_THU_segmentation
  ├── SubjectID_PicID.jpg   #raw data
  ├── SubjectID_PicID.json  #label

🗝️ Access and Usage

This dataset is built for academic use. Any commercial usage is banned.
There are two ways for downloads： OneDrive and Baidu Netdisk for researchers of different regions.
To access the dataset, you are supposed to download this data release agreement.
Please scan and dispatch the completed agreement via your institutional email to tjk24@mails.tsinghua.edu.cn and cc yuntaowang@tsinghua.edu.cn. The email should have the subject line 'ANFC_THU Access Request - your institution.' In the email, outline your institution's website and publications for seeking access to the ANFC_THU, including its intended application in your specific research project. The email should be sent by a faculty rather than a student.

⚙️ Setup

STEP1: bash setup.sh

STEP2: conda activate nailfold

STEP3: pip install -r requirements.txt

⚙️ Run Pipeline

Run Image Automated Analysis Pipeline

• Run full pipeline for specific image analysis: python Image_Analysis/nailfold_image_profile/overall_analysis.py

• full command: python Image_Analysis/nailfold_image_profile/overall_analysis.py --image_path "../Nailfold_Data_Tangshan/tangshan_data/tangshan_segmentation" --image_name "8_58452_5.jpg" --output_dir "./output_test" --visualize

• Run full pipeline for all images in image_path, just set image_name to '': python Image_Analysis/nailfold_image_profile/overall_analysis.py --image_path "../Nailfold_Data_Tangshan/tangshan_data/tangshan_segmentation" --image_name '' --output_dir "./output_results" --visualize

Run Video Automated Analysis Pipeline

• Analyze the all videos in $video_path_dict_file$ and return the velocity of the white blood cell: python Flow_Velocity_Measurement/video_profile.py --video_type ".mp4" --video_path ./Flow_Velocity_Measurement/video_sample --output_dir ./Flow_Velocity_Measurement/output_table/ --nailfold_pos_x 150 --nailfold_pos_y 100 --split_num 1 --pad_ratio 2 --video_path_dict_file ./outputs/aligned_video_path_dict.json --visualize

Video Profiles(WBC Count and Flow Velocity Measurement)

Analyze the video and return the velocity of the white blood cell:

• video example python Flow_Velocity_Measurement/video_profile.py --video_name "kp-6" --video_type ".mp4" --video_path ./Flow_Velocity_Measurement/video_sample --output_dir ./Flow_Velocity_Measurement/output/ --nailfold_pos_x 150 --nailfold_pos_y 100 --visualize --split_num 1 --pad_ratio 2

---

Models

UNet for segmetation

1. interface: from Image_Segmentation.image_segmentation.image2segment import t_images2masks

2. Please use config files under Image_Segmentation/image_segmentation/config.yaml

3. Train the model

• brief command

python Image_Segmentation/image_segmentation/main.py  --mode=train --num_epochs=60 --val_step=5 --batch_size=8

• whole command

python Image_Segmentation/image_segmentation/main.py  --mode=train 
--image_size=256 --img_ch=1 --output_ch=1 --batch_size=4 --num_workers=8 --augmentation_prob=0.7 --num_epochs=60 --num_epochs_decay=70 --lr=0.0002 --beta1=0.5 --beta2=0.999 --val_step=2 --log_step=2 --model_type=U_Net --model_path=./Image_Segmentation/image_segmentation/models --train_path=./Data_Preprocess/nailfold_data_preprocess/data/segment_dataset/train --valid_path=./Data_Preprocess/nailfold_data_preprocess/data/segment_dataset/test --test_path=./Data_Preprocess/nailfold_data_preprocess/data/segment_dataset/test --result_path=./Image_Segmentation/image_segmentation/result --cuda_idx=1

• epoch=60, lr = 0.0002, training result is [hightlight] Test Acc: 0.9616, SE: 0.6532, F1: 0.4871

• Training Log at ./Image_Segmentation/image_segmentation/result/U_Net-60-0.0002-70-0.7000.log

• Training Results Visualization at ./Image_Segmentation/image_segmentation/result/visualization/visualize_pred_gt

4. Test the model

python Image_Segmentation/image_segmentation/main.py  --mode=test --num_epochs=60 --val_step=5

---

RCNN for Keypoint Detection

interface: from Keypoint_Detection.nailfold_keypoint.detect_rcnn import t_images2kp_rcnn, t_images2masks_rcnn

Please use config files under ``

Dataset ./Keypoint_Detection/data/nailfold_dataset_crossing ./Keypoint_Detection/data/nailfold_dataset1

Train the model ./Keypoint_Detection/nailfold_keypoint

Test the model ``

Resnet18 for Classifier (normal or abnormal)

TBD

---

Toolbox Interface

for image segmentation

from Image_Segmentation.image_segmentation.image2segment import t_images2masks

for image keypoint detection and instance segmentation

from Keypoint_Detection.nailfold_keypoint.detect_rcnn import t_images2kp_rcnn, t_images2masks_rcnn

for video analysis

Analyze the video and return the velocity of the white blood cell: t_video_analysis(video_path, output_dir, pos: tuple, visualize: bool = False, split_num: int = 2, pad_ratio: float= 1)->typing.List[float]

---

Dataset Dir

1. original dataset ./data

2. all dataset used in each tasks ./Data_Preprocess/data

3. video frame dataset ./Data_Preprocess/data/new_data_frame

4. keypoints dataset original data patch: ./Keypoint_Detection/data original dataset for all: ./Keypoint_Detection/data/nailfold_dataset1 original dataset for crossing point ./Keypoint_Detection/data/nailfold_dataset_crossing

5. classifer ./Object_Detection/data

6. videos ./Video_Process/data /kp_videos for stabilized videos /videos are original one

---

checkpoints Dir

1. Segmentation model ./Image_Segmentation/image_segmentation/checkpoints/U_Net-60-0.0002-70-0.7000.pkl

2. keypoints ./Keypoint_Detection/nailfold_keypoint/checkpoints

3. classifier ./Object_Detection/nailfold_classifier/checkpoints

Citation

@inproceedings{zhao2024comprehensive,
  title={A Comprehensive Dataset and Automated Pipeline for Nailfold Capillary Analysis},
  author={Zhao, Linxi and Tang, Jiankai and Chen, Dongyu and Liu, Xiaohong and Zhou, Yong and Shi, Yuanchun and Wang, Guangyu and Wang, Yuntao},
  booktitle={2024 IEEE International Symposium on Biomedical Imaging (ISBI)},
  pages={1--5},
  year={2024},
  organization={IEEE}
}
}