SegNet for CT images pretrained weights

Question

I'm trying to train a SegNet for segmentation task on ct images (with Keras TF). I'm using VGG16 pretrained weights but I had a problem with the first convolutional layer because I'm using grayscale images but VGG was trained on rgb ones. I solved that using second method of this (can't use first method because requires too much memory). However it didn't help me, values are really bad (trained for 100 epochs).

Should I train the first convolutional layer from scratch?

Answer 1

You can try to add a Conv2D before the vgg. Something like :

> Your Input(shape=(height,width,1))

Conv2D(filters=3,kernel_size=1, padding='same',activation='relu')

> The VGG pretrained network (input = (height,width,3))

is interesting in your case because 1x1 convolution is usually employed to change the depth of your object.