Semi-automatic segmentation

a female doctor works on Semi-automatic segmentation of a brain MRI
By Marek Pitura

In the context of medical image analysis, the semi-automatic segmentation term can be encountered. Although automatic segmentation seems relatively intuitive, the “semi-automatic” description may cause some havoc for artificial intelligence algorithms. Here, we would like to provide a short explanation of what this semi-automatic property consist in and to present examples of its use.

What is the semi-automatic segmentation?

First, let us define segmentation as this is will be the task of our algorithm. Segmentation is the automatic outline of interesting image area in medical images. This may be a specific tissue, organ or tumour.

Next, to understand the essence of the semi-automatic segmentation, we need to have a look at the very algorithm used for outlining the interesting areas. When using methods based on deep learning techniques, it is usually composed, to put it simply, of the following:

Initially, the algorithm receives image files, e.g. in DICOM format.  Raw data is pre-processed and standardised and then a pre-trained deep neural network (DNN) model is launched, its results being able to be processed further to generate end outlines. If the entire process takes place with no human intervention, this is automatic segmentation. However, if any user interaction is required during the process, this is semi-automatic segmentation. Such interaction may take place e.g. during pre- or post-processing.

Semi-automatic segmentation – pre-processing

An example of a semi-automatic segmentation algorithm with a user interaction during pre-processing is an algorithm for coronary vessel segmentation in angio-CT examination prepared by us. Its input data is DICOM files with vessel centerline prepared manually by the user, i.e. a vessel route diagram.

Obviously, the preparation of such centerlines involves the user but it also provides more information to the deep network model and enables to obtain higher quality of vessel segmentation. The quality is improved here at the user’s time expense. This approach is adopted e.g. for images of inferior quality where the user’s expertise and intuition relating to anatomy and vessel route provides support to the model based on artificial intelligence.

Semi-automatic segmentation – post-processing

Examples of semi-automatic segmentation algorithm where the user interaction takes place during post-processing include all the cases where results needs verifying or manual adjustment, e.g. to improve their precision. The results of the manually “improved” segmentation are then used by subsequent algorithms or for further calculations.

A good example here is the algorithms for segmentation of coronary vessels and atheromas developed by GLI. Their objective is to measure the vessel lumen at the atheroma site. What is required here, is the vessel itself and the atheroma obstructing it. Both those components are segmented by dedicated deep learning models based on medical images and the results are presented for the user to verify or adjust them manually (using a dedicated tool). Only after they are approved, the vessel lumen is calculated at the indicated site.

To sum up this short introduction to the semi-automatic segmentation, it is worth stressing its advantages once again. It not only enables to improve or guarantee the suitable quality of the ultimate segmentations, but it can also reduce the processing time and the load on computing resources when compared to an algorithm which operates fully automatically, with no human support. Support is the key word here. Using human involvement, semi-automatic segmentation algorithms are able to supply the required solutions where the complete automation is not possible or where it does not bring about the required results.

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See the previous post by Marek Pitura: Ground-truth in a medical machine learning project