/BraTS20_Unet3d_AutoEncoder

3d unet and 3d autoencoder for automatical segmentation and feature extraction.

Primary LanguagePython

BraTS2020 Unet3d AutoEncoder

Data

Available here.

All BraTS multimodal scans are available as NIfTI files (.nii.gz) and describe a) native (T1) and b) post-contrast T1-weighted (T1Gd), c) T2-weighted (T2), and d) T2 Fluid Attenuated Inversion Recovery (T2-FLAIR) volumes.

Annotations comprise the GD-enhancing tumor (ET — label 4), the peritumoral edema (ED — label 2), and the necrotic and non-enhancing tumor core (NCR/NET — label 1).

multimodal slices with segmented mask:

3d projections of multimodal scans and segmented mask:

You can also see 3D data projection here

Formulation of the problem:

    1. Each pixel must be labeled “1” if it is part of one of the classes (NCR/NET — label 1, ED — label 2, ET — label 4), and “0” if not.
    1. Make a prediction of age and survival days for each unique identifier in the data.

Solution

    1. For automatical segmentation was used Unet3d with group normal layers. - unet
    1. To predict age and number of days of survival - the autoencoder was trained to scale the space from 4 * 240 * 240 * 150 to 512, then statistical values, and hidden representations were extracted for each identifier in the data, encoded by the pretrained autoencoder. after wich SVR was trained on this data. - autoencoder

Result

Unet Result:

AutoEncoder Result:

More results can be seen here or here.