f6da3707-8ae6-46e5-9ddd-df03ca383612
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Create an 3D UNET model for 3D array segmentation from scratch.
fc010f62-4ebf-46cd-893f-180cde59f0f5
f6da3707-8ae6-46e5-9ddd-df03ca383612
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Here's an example of how you can create a 3D UNet model for 3D array segmentation from scratch in Keras:

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import keras
from keras.models import Model
from keras.layers import Input, Conv3D, MaxPooling3D, UpSampling3D, concatenate

def unet_3d(input_shape, num_classes):
    inputs = Input(input_shape)
    
    # Contracting path
    conv1 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(inputs)
    conv1 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(conv1)
    pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1)
    
    conv2 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(pool1)
    conv2 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(conv2)
    pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2)
    
    conv3 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(pool2)
    conv3 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(conv3)
    pool3 = MaxPooling3D(pool_size=(2, 2, 2))(conv3)
    
    conv4 = Conv3D(512, (3, 3, 3), activation='relu', padding='same')(pool3)
    conv4 = Conv3D(512, (3, 3, 3), activation='relu', padding='same')(conv4)
    pool4 = MaxPooling3D(pool_size=(2, 2, 2))(conv4)
    
    # Expansive path
    up5 = concatenate([UpSampling3D(size=(2, 2, 2))(pool4), conv4], axis=-1)
    conv5 = Conv3D(512, (3, 3, 3), activation='relu', padding='same')(up5)
    conv5 = Conv3D(512, (3, 3, 3), activation='relu', padding='same')(conv5)
    
    up6 = concatenate([UpSampling3D(size=(2, 2, 2))(conv5), conv3], axis=-1)
    conv6 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(up6)
    conv6 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(conv6)
    
    up7 = concatenate([UpSampling3D(size=(2, 2, 2))(conv6), conv2], axis=-1)
    conv7 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(up7)
    conv7 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(conv7)

up8 = concatenate([UpSampling3D(size=(2, 2, 2))(conv7), conv1], axis=-1)
    conv8 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(up8)
    conv8 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(conv8)
    
    outputs = Conv3D(num_classes, (1, 1, 1), activation='softmax')(conv8)
    
    model = Model(inputs=inputs, outputs=outputs)
    
    return model

This code creates a 3D UNet model that takes a 3D input array of a specified shape and outputs a segmentation of num_classes. The contracting path consists of multiple convolutional and max pooling layers, and the expansive path consists of multiple up-sampling and concatenation layers. The final layer is a Conv3D layer with num_classes filters and a softmax activation function, which outputs a segmented 3D array.