Module ukat.segmentation.whole_kidney
Classes
class Segmentation (pixel_array, affine, post_process=True, binary=True, weights=None)-
Attributes
volumes:dict- Total, left and right kidney volumes in milliliters.
Initialise a whole kidney segmentation class instance.
Parameters
pixel_array:np.ndarray- Array containing a T2-weighted FSE image.
affine:np.ndarray- A matrix giving the relationship between voxel coordinates and world coordinates.
post_process:bool, optional- Default True Keep only the two largest connected volumes in the mask. Note this may cause issue with subjects that have more or less than two kidneys.
binary:bool, optional- Default True. If True, the mask returned will be an array of ints, where 1 represents voxels that are renal tissue and 0 represents voxels that are not renal tissue. If False, the mask returned will be the probability that each voxel is renal tissue.
weights:str, optional- Default 'none'. The path to custom neural network weights. If 'none', the default, all-purpose, weights will be downloaded from the internet and used.
Expand source code
class Segmentation(nib.Nifti1Image): """ Attributes ---------- volumes : dict Total, left and right kidney volumes in milliliters. """ def __init__(self, pixel_array, affine, post_process=True, binary=True, weights=None): """Initialise a whole kidney segmentation class instance. Parameters ---------- pixel_array : np.ndarray Array containing a T2-weighted FSE image. affine : np.ndarray A matrix giving the relationship between voxel coordinates and world coordinates. post_process : bool, optional Default True Keep only the two largest connected volumes in the mask. Note this may cause issue with subjects that have more or less than two kidneys. binary : bool, optional Default True. If True, the mask returned will be an array of ints, where 1 represents voxels that are renal tissue and 0 represents voxels that are not renal tissue. If False, the mask returned will be the probability that each voxel is renal tissue. weights : str, optional Default 'none'. The path to custom neural network weights. If 'none', the default, all-purpose, weights will be downloaded from the internet and used. """ super().__init__(pixel_array, affine) self.pixel_array = pixel_array self._nifti = nib.Nifti1Image(self.pixel_array, self.affine) self._seg_obj = Tkv(self._nifti) self._mask = self._seg_obj.get_mask(post_process=post_process, binary=binary, weights_path=weights) self._kidneys = (self._mask > 0.5) * 1 self._kidneys[:self.shape[0]//2] *= 2 self.volumes = {'total': self._calculate_volume(self._mask > 0.5), 'left': self._calculate_volume(self._kidneys == 1), 'right': self._calculate_volume(self._kidneys == 2)} def get_mask(self): """ Returns the estimated mask from the provided input data. Returns ------- mask : np.ndarray The estimated mask. """ return self._mask def get_kidneys(self): """ Returns a mask where 0 represents voxels that are not renal tissue, 1 represents voxels that are the left kidney and 2 represents voxels that are the right kidney. Returns ------- mask : np.ndarray Mask with each kidney represented by a different int. """ return self._kidneys def get_left_kidney(self): """ Returns a binary mask where True is the left kidney. Returns ------- left_kidney : np.ndarray Binary mask of left kidney. """ left_kidney = (self._kidneys == 1) * 1 return left_kidney def get_right_kidney(self): """ Returns a binary mask where True is the right kidney. Returns ------- right_kidney : np.ndarray Binary mask of right kidney. """ right_kidney = (self._kidneys == 2) * 1 return right_kidney def get_volumes(self): """ Return the volume of each kidney and total kidney volume in milliliters. Returns ------- volumes : dict Total, left and right kidney volumes in milliliters. """ return self.volumes def save_volumes_csv(self, path): """ Save the total, left and right kidney volumes to a csv file. Parameters ---------- path : str Path to the desired csv file. """ with open(path, 'w', newline='') as f: w = csv.DictWriter(f, self.volumes.keys()) w.writeheader() w.writerow(self.volumes) def get_tkv(self): """ Get the total kidney volume in milliliters. Returns ------- tkv : float Total kidney volume """ return self.volumes['total'] def get_lkv(self): """ Get the left kidney volume in milliliters. Returns ------- lkv : float Left kidney volume """ return self.volumes['left'] def get_rkv(self): """ Get the right kidney volume in milliliters. Returns ------- rkv : float Right kidney volume """ return self.volumes['right'] def to_nifti(self, output_directory=os.getcwd(), base_file_name='Output', maps='all'): """Exports masks to NIFTI. Parameters ---------- output_directory : string, optional Path to the folder where the NIFTI files will be saved. base_file_name : string, optional Filename of the resulting NIFTI. This code appends the extension. Eg., base_file_name = 'Output' will result in 'Output.nii.gz'. maps : list or 'all', optional List of maps to save to NIFTI. This should either the string "all" or a list of maps from ["mask", "left", "right", "individual"] """ os.makedirs(output_directory, exist_ok=True) base_path = os.path.join(output_directory, base_file_name) if maps == 'all' or maps == ['all']: maps = ['mask', 'left', 'right', 'individual'] if isinstance(maps, list): for result in maps: if result == 'mask': mask_nifti = nib.Nifti1Image(self.get_mask(), self.affine, dtype=np.uint16) nib.save(mask_nifti, base_path + '_mask.nii.gz') elif result == 'left': left_nifti = nib.Nifti1Image(self.get_left_kidney(), self.affine, dtype=np.uint16) nib.save(left_nifti, base_path + '_left_kidney.nii.gz') elif result == 'right': right_nifti = nib.Nifti1Image(self.get_right_kidney(), self.affine, dtype=np.uint16) nib.save(right_nifti, base_path + '_right_kidney.nii.gz') elif result == 'individual': ind_nifti = nib.Nifti1Image(self.get_kidneys(), self.affine, dtype=np.uint16) nib.save(ind_nifti, base_path + '_individual_kidneys.nii.gz') else: raise ValueError('No NIFTI file saved. The variable "maps" ' 'should be "all" or a list of maps from ' '"["mask", "left", "right", "individual"]".') def _calculate_volume(self, mask): mask = mask > 0.5 return np.sum(mask) * np.prod(self.header.get_zooms()) / 1000Ancestors
- nibabel.nifti1.Nifti1Image
- nibabel.nifti1.Nifti1Pair
- nibabel.analyze.AnalyzeImage
- nibabel.spatialimages.SpatialImage
- nibabel.dataobj_images.DataobjImage
- nibabel.filebasedimages.SerializableImage
- nibabel.filebasedimages.FileBasedImage
Class variables
var header_class : type[Nifti1Header]-
Class for NIfTI1 header
The NIfTI1 header has many more coded fields than the simpler Analyze variants. NIfTI1 headers also have extensions.
Nifti allows the header to be a separate file, as part of a nifti image / header pair, or to precede the data in a single file. The object needs to know which type it is, in order to manage the voxel offset pointing to the data, extension reading, and writing the correct magic string.
This class handles the header-preceding-data case.
Instance variables
prop header : FileBasedHeader-
Expand source code
@property def header(self) -> FileBasedHeader: return self._header
Methods
def get_kidneys(self)-
Returns a mask where 0 represents voxels that are not renal tissue, 1 represents voxels that are the left kidney and 2 represents voxels that are the right kidney.
Returns
mask:np.ndarray- Mask with each kidney represented by a different int.
def get_left_kidney(self)-
Returns a binary mask where True is the left kidney.
Returns
left_kidney:np.ndarray- Binary mask of left kidney.
def get_lkv(self)-
Get the left kidney volume in milliliters. Returns
lkv:float- Left kidney volume
def get_mask(self)-
Returns the estimated mask from the provided input data.
Returns
mask:np.ndarray- The estimated mask.
def get_right_kidney(self)-
Returns a binary mask where True is the right kidney.
Returns
right_kidney:np.ndarray- Binary mask of right kidney.
def get_rkv(self)-
Get the right kidney volume in milliliters. Returns
rkv:float- Right kidney volume
def get_tkv(self)-
Get the total kidney volume in milliliters. Returns
tkv:float- Total kidney volume
def get_volumes(self)-
Return the volume of each kidney and total kidney volume in milliliters.
Returns
volumes:dict- Total, left and right kidney volumes in milliliters.
def save_volumes_csv(self, path)-
Save the total, left and right kidney volumes to a csv file.
Parameters
path:str- Path to the desired csv file.
def to_nifti(self, output_directory='/home/runner/work/ukat/ukat', base_file_name='Output', maps='all')-
Exports masks to NIFTI.
Parameters
output_directory:string, optional- Path to the folder where the NIFTI files will be saved.
base_file_name:string, optional- Filename of the resulting NIFTI. This code appends the extension. Eg., base_file_name = 'Output' will result in 'Output.nii.gz'.
maps:listor'all', optional- List of maps to save to NIFTI. This should either the string "all" or a list of maps from ["mask", "left", "right", "individual"]