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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
The text was updated successfully, but these errors were encountered:
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.
The text was updated successfully, but these errors were encountered: