Computer Vision News - March 2023

26 Medical Image Segmentation In this example, we load a pre-trained DenseNet model and replace its final classification layer with a 1x1 convolution. We then add an upsampling layer to resize the output to match the input image size. During training, we can use a loss function such as binary cross- entropy to compare the output segmentation map to the ground truth labels. Another approach to using DenseNet for medical image segmentation is to use a 3D convolutional network. This is particularly useful for segmentation tasks that involve volumetric data such as MRI scans. Now just for the sake of inclusivity and comparison, I want to show you an example of a 3D convolutional network implemented using DenseNet in Keras: from keras.models import Model from keras.layers import Input, Conv3D, Dense, Dropout, GlobalAveragePooling3D from keras.applications.densenet import DenseNet121 def build_3d_densenet(input_shape, n_classes): inputs = Input(shape=input_shape) # Load pretrained Densenet model base_model = DenseNet121(input_shape=input_shape, include_top=False) # Add 3D convolutional layers x = Conv3D(64, (3, 3, 3), padding='same')(inputs) x = base_model(x) x = Conv3D(64, (3, 3, 3), padding='same')(x) x = GlobalAveragePooling3D()(x) x = Dropout(0.5)(x) x = Dense(256, activation='relu')(x) x = Dropout(0.5)(x) outputs = Dense(n_classes, activation='softmax')(x) # Build the model model = Model(inputs, outputs) return model In this example, we load a pre-trained DenseNet model and add 3D convolutional layers to the beginning and end of the network. We then train the model on volumetric data and use a Softmax activation function to produce a segmentation map with multiple classes. But how about applying segmentation with a different data modality? Medical image segmentation is important and can be used with images from different modalities such as MRIs, CT scans, and X-rays. One common application of medical image segmentation is the segmentation of tumors, which is an essential step in the diagnosis and treatment of cancer.