<p>Just in case someone would be interested in how I tackled this problem:</p>

<p>For segmentation of grayscale images, I found out this architecture: <a href="https://arxiv.org/abs/1505.04597" rel="nofollow noreferrer">U-Net</a>
The tensorflow adaptation by Akeret et al. can be found in <a href="https://github.com/jakeret/tf_unet" rel="nofollow noreferrer">Github - UNet</a></p>

<p>I did not use API Object detection in the end since I was not sure how well the pre-trained models would perform in 1-Channel Images since they have been created for RGB-images.</p>
Pre-trained Object Detection API model for greyscale images (1 channel)
<p>I want to detect features, and possibly segmented them, in CT scan images (greyscale images) by means of the Object Detection API from Tensorflow. So I have two questions:</p>

<p>-Is there any pre-trained model within the Object Detection API that takes greyscale images as inputs for fine tuning training?</p>

<p>-Or, How can I train exisiting models, that are for RGB images, so that I can train them with greyscale images? Which modifications should be made in the code?</p>