Calculate tumor volume from pixel masks

[drivendataorg]

Calculate tumor volume from pixel masks

[vessemer]

I'm a bit confused: how we are going to calculate the real volume of an area while the function does not receive the CT meta data. Also, can you please clarify how the output from the predict method will be serialised? And last: is it necessary to provide the centroids, since all centroids may be re-estimated via the connected components analysis?

[reubano]

Hi @vessemer

I'm a bit confused: how we are going to calculate the real volume of an area while the function does not receive the CT meta data.

Good point! I think one way to approach this is to create a new function that outputs metadata, and then pass in that metadata as a third argument. I see you've assumed something similar in your PR via voxel_shape.

Also, can you please clarify how the output from the predict method will be serialised?

That will be up to whoever implements the predict function. I see you have assumed npy files. That should be perfectly fine as a stub for now. In the event a different serialization method is chosen, it shouldn't be too difficult to make the appropriate updates.

And last: is it necessary to provide the centroids, since all centroids may be re-estimated via the connected components analysis?

It is the job of algorithms.identify.trained_model.predict to calculate centroids. And it is this centroids argument that is passed to algorithms.segment.trained_model.predict. segment.trained_model.predict in turn calls segment.trained_model.calculate_volume. While it would be possible for calculate_volume to take dicom_path as an argument, and then calculate centriods on it's own, we prefer to uncouple those steps and have a canonical function dedicated to that task.

[vessemer]

@reubano, thanks for making it clear. I guess I've considered all your proposition, by this commit.
Is there something else I need take into account?

[reubano]

Hi @vessemer, see #78 (comment).

[reubano]

Closed via #78