3D Implementation of the Tiny Yolo v2 model to jointly detect nucleus-Golgi pairs in 3D Microscopy Images
Firstly, generate a dataset with input images and corresponding bounding boxes. Input images should be saved as .tif files, with dimensions (Z,X,Y,C). Bounding boxes should be saved in a numpy array (.npy object) of size (N,6), where N denotes the total number of bounding boxes in the corresponding image, and for each bounding box it contains the coordinates: [xmin, xmax, ymin, ymax, zmin, zmax] (in this order).
Thereafter, change the following parameters in BboxToXml.py and run it:
img_dir: directory containing the .tif images (Z,X,Y,C)bbox_dir: directory containing the .npy objects with the bounding boxes (N,6)xml_dir: directory where the .xml files will be saved
This will generate the dataset in the appropriate format to train the model.
If you want to perform data augmentation, change the img_dir, bbox_dir, and maxpatches parameter in dataaugmentation.py and run it, where:
img_dir: directory containing the .tif images (Z,X,Y,C)bbox_dir: directory containing the .npy objects with the bounding boxes (N,6)maxpatches: number of augmented image patches
It performs z-axis aligned rotations in the range (0, 360◦) with steps of size 90◦, horizontal flips, vertical flips and intensity variations.
Organize the training and validation images (.tif files, size=(DEPTH, WIDTH, HEIGHT, CHANNELS)) and annotations (in PASCAL VOC format) as follows:
datasetyolo
├── train
│ ├── images
│ └── annot
└── val
├── images
└── annot
An example of a 2D Code to generate annotations in VOC format can be found here.
To train the model follow the steps provided in the jupyter notebook.
Python 3.5.2, Tensorflow-GPU 1.9.0, Keras 2.2.4 and other packages listed in requirements.txt.
Code adapted from: https://github.com/experiencor/keras-yolo2 and https://github.com/rankofootball/3DYolo2.