christianpayer

Regarding your observation on testing being stuck, I identified that the spine_postprocessing function makes some troubles when the networks are far from being converged. As I currently don't have time...

Unfortunately, as you also recognized, due to the large input images, the runtime of the training scripts is limited by the data augmentation, which runs entirely on the CPU. There...

I think the `reorient_reference_to_rai.py` script is correct and the landmark files fit to the reoriented orientation. At least on my PC it is working. @zengchan can you confirm that the...

I think the `proprocess_landmarks.py` script is also fine. At least, it works well for me. Are you trying to generate the landmarks for the VerSe2019 dataset or the VerSe2020 dataset?...

@zengchan Yes, this is the line that you need to do to generate the landmarks for VerSe2020. Regarding your broken pipe error, it could have multiple causes. First, make sure...

Yes, this makes sense. The augmentation operations seem to be the bottleneck. `refill_queue_factor = 0.5` will make it much faster, but it is really problematic. When the client takes an...

As the VerSe2020 dataset contains more images, while the images themselves are larger, they do not fit into the default cache size of the CachedImageDataSource. Furthermore, some images are saved...

I also occasionally observed a memory leak when using `use_multiprocessing = False` and connecting multiple times to the same server. I did not have time to more deeply investigate in...

No problem! I'm glad if people are using our methods and I can help fixing issues.

No, it is not. In this repository, we calculate the id_rate and average MLD per landmark. I think, in the official VerSe challenge paper, they calculate the id_rate and MLD...