Reply To: Using UNet for Automatic Segmentation of CT Lung Images

#53716
Peter Herrmann
Participant
Participant
    @pieth

    Dear Youssef,

    I would like to program a UNet from scratch with HAIBAL in order to train it with new data.

    My goal within our research project is a multiclass segmentation, i.e. not just one class (the lungs), but several classes (lungs, heart, trachea, etc.). There are now also very interesting modifications of UNet to optimize the segmentation process (e.g. 3D U-Net, Attention U-Net, Inception U-Net, 5 Residual U-Net, Recurrent Convolutional Network, Dense U-Net, U-Net++, Adversarial U-Net), which cannot be programmed with DeepLTK.

    In order to program these new U-Net modifications, however, I first have to master the basics in HAIBAL!
    1.) Development of an encoder-decoder (SegNet) with HAIBAL and
    2.) Development of a U-Net with HAIBAL
    Youssef, I need your help for this.

    Maybe the examples in Keras will help:

    UNet: https://pyimagesearch.com/2022/02/21/u-net-image-segmentation-in-keras/

    https://github.com/divamgupta/image-segmentation-keras

    https://keras.io/examples/vision/oxford_pets_image_segmentation/

    https://modelzoo.co/model/popular-image-segmentation-models