Constructing UNET and FCN8 models for left atrium segmentation

My Goal is to create two image segmentation models that can identify the left atrium segmentation from MRI scans. These models are Fully Convolutional Network (FCN-8) and UNET. The accuracy of the model will be measured using the binary_crossentropy loss and both predicted results will be compiled to GIFs.
Side Note: Model structures are learned from Coursera Course, Advanced Techniques TensorFlow

Source: http://medicaldecathlon.com/
License: https://creativecommons.org/licenses/by-sa/4.0/

Extract mri and segmentation labels using nibabel.

Covert extractednumpy arrays to TF-Records, a data format that is believed to be the best for training when using TensorFlow Keras
Additionally, this format saves a lot of storage space on your PC :D! (70~80% storage savings)

Build models, FCN-8 and UNET with
Final Output Layer: "Sigmoid"
Loss: "Binary CrossEntropy"
Optimizer: "Adam"
Side Note: These structures can also be used for multi-class segmentation problem but changes need to apply accordingly