itk_torch_bridge.metatensor_to_itk_image() incorrect orientation

[ibro45]

Describe the bug
I have been trying to use itk_torch_bridge.metatensor_to_itk_image() to save the predicted masks, but it consistently fails to save them in the correct orientation. I initially suspected that something went wrong in the inverse transforms I was doing before saving, but I could not find any problem there. Finally, I looked into the metatensor_to_itk_image() itself.

Apparently, loading an image with MONAI, converting it to ITK, and then writing it will result in a different orientation, as can be seen in the Slicer screenshots below.

To Reproduce
Use any scan. For reproducibility, I used this one: https://github.com/google-deepmind/tcia-ct-scan-dataset/blob/master/nrrds/test/oncologist/0522c0017/CT_IMAGE.nrrd

import itk
from monai.data.itk_torch_bridge import metatensor_to_itk_image
from monai.transforms import Compose, LoadImaged, EnsureTyped, EnsureChannelFirstd

transforms = Compose(
        [
            LoadImaged(keys=["image"]),
            EnsureTyped(keys=["image"]),
            EnsureChannelFirstd(keys=["image"]),
        ]
    )

out = transforms({"image": "./CT_IMAGE.nrrd"})
image = out["image"]

itk_image = metatensor_to_itk_image(image, channel_dim=0, dtype=image.dtype)
itk.imwrite(itk_image, "metatensor_to_itk_image.nrrd", True)

Expected behavior
The original and the saved image should be identical.

Screenshots

Original:

---

metatensor_to_itk_image()-ed:

Environment

================================
Printing MONAI config...

MONAI version: 1.3.dev2337
Numpy version: 1.25.2
Pytorch version: 2.0.1
MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False
MONAI rev id: 57e24b54faa4e7aea1b2d28f9408311fd34543b1
MONAI file: /home//miniconda3/envs/_aim/lib/python3.11/site-packages/monai/init.py

Optional dependencies:
Pytorch Ignite version: NOT INSTALLED or UNKNOWN VERSION.
ITK version: 5.3.0
Nibabel version: 5.1.0
scikit-image version: NOT INSTALLED or UNKNOWN VERSION.
scipy version: NOT INSTALLED or UNKNOWN VERSION.
Pillow version: 9.4.0
Tensorboard version: 2.14.0
gdown version: NOT INSTALLED or UNKNOWN VERSION.
TorchVision version: 0.15.2
tqdm version: 4.66.1
lmdb version: NOT INSTALLED or UNKNOWN VERSION.
psutil version: 5.9.0
pandas version: 2.1.0
einops version: NOT INSTALLED or UNKNOWN VERSION.
transformers version: NOT INSTALLED or UNKNOWN VERSION.
mlflow version: NOT INSTALLED or UNKNOWN VERSION.
pynrrd version: 1.0.0
clearml version: NOT INSTALLED or UNKNOWN VERSION.

For details about installing the optional dependencies, please visit:
https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies

================================
Printing system config...

System: Linux
Linux version: Ubuntu 20.04.6 LTS
Platform: Linux-5.4.0-153-generic-x86_64-with-glibc2.31
Processor: x86_64
Machine: x86_64
Python version: 3.11.5
Process name: python
Command: ['python', '-c', 'import monai; monai.config.print_debug_info()']
Open files: [popenfile(path='/home/ibro/.cursor-server/data/logs/20230913T092831/ptyhost.log', fd=19, position=0, mode='a', flags=33793), popenfile(path='/home/ibro/.cursor-server/data/logs/20230913T092831/remoteagent.log', fd=20, position=4072, mode='a', flags=33793)]
Num physical CPUs: 24
Num logical CPUs: 48
Num usable CPUs: 48
CPU usage (%): [37.5, 1.1, 0.0, 1.1, 1.1, 0.0, 23.6, 6.8, 3.4, 27.0, 11.2, 28.4, 29.2, 0.0, 9.1, 0.0, 13.6, 0.0, 100.0, 0.0, 1.1, 4.5, 100.0, 0.0, 0.0, 0.0, 0.0, 1.1, 0.0, 35.2, 4.5, 21.8, 24.4, 0.0, 1.1, 2.3, 3.4, 1.1, 0.0, 0.0, 0.0, 3.4, 0.0, 5.7, 9.2, 3.4, 0.0, 3.4]
CPU freq. (MHz): 3
Load avg. in last 1, 5, 15 mins (%): [11.8, 11.8, 11.8]
Disk usage (%): 65.3
Avg. sensor temp. (Celsius): UNKNOWN for given OS
Total physical memory (GB): 251.8
Available memory (GB): 217.9
Used memory (GB): 30.2

================================
Printing GPU config...

Num GPUs: 4
Has CUDA: True
CUDA version: 11.8
cuDNN enabled: True
NVIDIA_TF32_OVERRIDE: None
TORCH_ALLOW_TF32_CUBLAS_OVERRIDE: None
cuDNN version: 8700
Current device: 0
Library compiled for CUDA architectures: ['sm_37', 'sm_50', 'sm_60', 'sm_61', 'sm_70', 'sm_75', 'sm_80', 'sm_86', 'sm_90', 'compute_37']
GPU 0 Name: Quadro RTX 8000
GPU 0 Is integrated: False
GPU 0 Is multi GPU board: False
GPU 0 Multi processor count: 72
GPU 0 Total memory (GB): 47.5
GPU 0 CUDA capability (maj.min): 7.5
GPU 1 Name: Quadro RTX 8000
GPU 1 Is integrated: False
GPU 1 Is multi GPU board: False
GPU 1 Multi processor count: 72
GPU 1 Total memory (GB): 47.5
GPU 1 CUDA capability (maj.min): 7.5
GPU 2 Name: Quadro RTX 8000
GPU 2 Is integrated: False
GPU 2 Is multi GPU board: False
GPU 2 Multi processor count: 72
GPU 2 Total memory (GB): 47.5
GPU 2 CUDA capability (maj.min): 7.5
GPU 3 Name: Quadro RTX 8000
GPU 3 Is integrated: False
GPU 3 Is multi GPU board: False
GPU 3 Multi processor count: 72
GPU 3 Total memory (GB): 47.5
GPU 3 CUDA capability (maj.min): 7.5

Additional context
Otherwise, this is a really cool feature, easy ITK <-> MONAI conversion was something that I really missed before, thanks everyone!

[wyli]

thanks, that's a good point, I'll try to enhance the code logic as the current metatensor_to_itk_image assumes itk convention. for now please use LoadImaged(keys=["image"], affine_lps_to_ras=False, reader="ITKReader").

[ibro45]

Thanks! Is there a way to achieve that not by changing the LoadImaged args, but by applying something onto the MetaTensor directly?

[wyli]

yes, you can also modify the affine right before metatensor_to_itk_image

image.affine = monai.data.utils.orientation_ras_lps(image.affine)
metatensor_to_itk_image(image...

[ibro45]

That is great!! Thank you so much 🎉