rwindsor1 / spinenet Goto Github PK

I just installed SpineNet via your guide, and I believe the following packages are missing in your requirements.txt:

• matplotlib

Additionally,
`
class SpineNet:

def __init__(
    self, device: bool = "cuda:0", verbose: bool = True, scan_type: str = "lumbar"
) -> None:
    """
    Initialize instance of spinenet for (1) detecting and labelling vertebrae 
    (2) Performing radiological grading for common spinal degenerative changes in T2 sagittal lumbar scans.

    Parameters
    ----------
    device : str, optional
        The pytorch-style device to use for the model. The default is "cuda:0". If you not using CUDA-enabled machine, you can use "cpu" (although this will slow performance).

`

type mismatch between device being a bool and an optional str.

Best regards
Hendrik

To recreate, create a fresh install, using latest libraries:

python3 -m venv venv
. ./venv/bin/activate
python3 -m pip install --upgrade pip
python3 -m pip install -r requirements
jupyter lab

open tutorials/01-quickstart.ipynb in jupyter lab

Run all cells. Cell 3 fails with:
AttributeError: ptp was removed from the ndarray class in NumPy 2.0. Use np.ptp(arr, ...) instead.

In the tutorial page thre is an example of NIFTI predictions whre the pixel spacing is
sx, sy, sz = image.header.get_zooms() # get pixel spacings

However when calling the detection function is specified as
vert_dicts = spnt.detect_vb(scan.volume, scan.pixel_spacing[0])

In the case tat would like to use the both pixel spacing the function detect_vb throus an error, despite in teh definition in theory allow .
def detect_vb(
self,
volume : np.ndarray,
pixel_spacing : Union[np.ndarray, List[float], torch.Tensor],
debug: bool = False,
penalise_skips: bool = True,
remove_single_slice_detections: bool = True,
) -> VertDicts:
"""
Use SpineNet to detect and label vertebral bodies in a volume.

    Parameters
    ----------
    volume : np.ndarray
        The volume to detect vertebrae in. Should have shape (height,width, number of sagittal slices).
    pixel_spacing : Union[np.ndarray, List[float], torch.Tensor]
        The pixel spacing of the volume, specifically the distance between adjacent pixels in the sagittal direction.
        This has order height, width

Hi, I am currently testing SpinenetV2 on the spinegeneric dataset and I am experiencing labeling issues and missed detections for T2w images, as displayed on the following images:

Could you explain me what is wrong with those images ? And how they are different from the training dataset used for SpinenetV2 ? Because according to your Readme.md T2w contrasts should be handled.

Numpy np.bool is deprecated since v1.20 I would suggest that you upload the file spinet/utils/gen_utils poky2
line 370 mask = np.zeros(shape, dtype=np.bool_)
I can create a merge request with the cahnge if you would like

pre-commit linters in .pre-commit-config.yaml don't currently pass on the entire codebase

Hi, I'm currently doing a benchmark of methods to detect vertebral discs (or vertebral bodies) on MRI scans and I truly believe that your method is interesting and could be part of this benchmark. Therefore, I would like to know if it was possible to have access to your training scripts in order to qualitatively compare this method with other deep learning based methods.