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3D medical data annotation is an important part of training 3D image segmentation models and promotes disease diagnosis and treatment prediction, but 3D medical data annotation relies on time-consuming and laborious manual annotation by professionals. The low labeling efficiency leads to the lack of large-scale labeling data, which seriously hinders the development of medical AI. To solve this problem, we launched EISeg-Med3D, an intelligent annotation platform for 3D medical images based on interactive segmentation.
EISeg-Med3D is a 3D slicer extension for performing Efficient Interactive Segmentation on Medical image in 3D medical images. Users will guide a deep learning model to perform segmentation by providing positive and negative points. It is simple to install, easy to use and accurate, which can achieve ten times efficiency lift compares to manual labelling. At present, our medical annotation provides the try-on experience on the specified MRI vertebral data. If there is a need for 3D annotation on other data, you can make a contact.
Efficient:Each category only needs a few clicks to generate 3d segmentation results, ten times efficient compares to time-consuming and laborious manual annotation.
Accurate:The mIOU can reach 0.85 with only 3 clicks. with the segmentation editor equipped with machine learning algorithm and manual annotation, 100% accuracy is right on your hand.
Convenient:Install our plugin within three steps; labeling results and progress are automatically saved; the transparency of labeling results can be adjusted to improve labeling accuracy; user-friendly interface interaction makes labeling worry-free and hassle-free。
The EISeg-Med 3D model structure is shown in the figure below. We innovatively introduce the 3D model into the medical interactive segmentation, and modify the point sampler module and the click feature extrator of RITM to be compatible with 3D data, so as to directly label 3D medical images. Compared with 2D interactive annotation on 3D images, our method is more acurate and more efficient.
The overall model includes two parts: click generation module, click feature generation module, click feature and input image fusion and segmentation model:
EISeg-Med3D Model
The overall process of using EISeg-Med3D is shown in the figure below. We will introduce in the following three steps including environment installation, model and data download and user guide. The steps to use our platform can also be found in the video in the introduction.
The overall process
The process of AI-assisted labelling
Download and install 3D slicer:Slicer website
Download code of EISeg-Med3D:
git clone https://github.com/PaddlePaddle/PaddleSeg.git
import sys
import os
sys.executable # 'D:/slicer/Slicer 5.0.3/bin/PythonSlicer.exe'
os.system(f"'{sys.executable}' -m pip install paddlepaddle-gpu==2.3.1.post111 -f https://www.paddlepaddle.org.cn/whl/windows/mkl/avx/stable.html")
The final line should output 0. Anything else indicates the installation has failed. If you started 3D Slicer from a terminal, you should be able to see pip install progress there.
Enter the corresponding subprocess.py change shell=True in Popen class.
Currently we provide trial experience on the following models and data:
Data | Model | Links |
---|---|---|
MRI-spine | Interactive Vnet | pdiparams-pw: 6ok7 | pdmodel-pw: sg80 | Spine Data |
Model Settings
:...
button of Model Path
, choose local file of .pdodel
suffix and load .pdiparams
file in Param Path
in the same way.Load Static Model
button. And Sucessfully loaded model to gpu!
window will be prompt is the model is loaded successfully.
Data Folder
, choose the folder that you saved your downloaded data. And all of the data under that folder will be loaded and you can see the labelling status of loaded data in Progress
.
Prev Scan
button to see the previous image.Next Scan
button to see the next image.
Add/Remove
in Segment Editor
to add or remove the label. You can change the name of added label by double click the label item.Positive Point
or Negative Point
to enter interactive label mode。Finish Segment
button to finish annotation of current segment, you can further edit the annotatioin using tools in segment editor or you can repeat previous step to label next category. If you finished the annotation on this case, you can click on the Finish Scan
button.
Annotation Progress
of Progress
, you can checkout the labelling progress of loaded images.Annotation Progress
will jump to the corresponding image.
In the future, we want to continue to develop EISeg-Med3D in these aspects, welcome to join our developer team.
EISeg-Med3D is released under the Apache 2.0 license.
Thanks to Idea icons created by Vectors Market - Flaticon for facsinating icons.
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