def segment(self, model: SegModel, tissue: Tissue, use_rms: bool = False):
"""Segment tissue in scan.
Args:
model (SegModel): Model to use for segmenting scans.
tissue (Tissue): The tissue to segment.
use_rms (`bool`, optional): Use root-mean-square of echos for segmentation (preferred). Defaults to `False`.
Returns:
MedicalVolume: Binary mask for segmented region.
"""
# Use first echo for segmentation.
logging.info("Segmenting {}...".format(tissue.FULL_NAME))
if use_rms:
segmentation_volume = self.calc_rms()
else:
segmentation_volume = self.volumes[0]
# Segment tissue and add it to list.
mask = model.generate_mask(segmentation_volume)
tissue.set_mask(mask)
self.__add_tissue__(tissue)
return mask
def segment(self, model: SegModel, tissue: Tissue, use_rss: bool = False):
"""Segment tissue in scan.
Args:
model (SegModel): Model to use for segmenting scans.
tissue (Tissue): The tissue to segment.
use_rss (`bool`, optional): If ``True``, use root-sum-of-squares (RSS) of
echos for segmentation (preferred for built-in methods).
If ``False``, use first echo for segmentation.
Returns:
MedicalVolume: Binary mask for segmented region.
"""
tissue_names = (
", ".join([t.FULL_NAME for t in tissue])
if isinstance(tissue, Sequence)
else tissue.FULL_NAME
)
logging.info(f"Segmenting {tissue_names}...")
if use_rss:
segmentation_volume = self.calc_rss()
else:
# Use first echo for segmentation.
segmentation_volume = self.volumes[0]
# Segment tissue and add it to list.
mask = model.generate_mask(segmentation_volume)
if isinstance(mask, dict):
if not isinstance(tissue, Sequence):
tissue = [tissue]
for abbreviation, tis in zip([t.STR_ID for t in tissue], tissue):
tis.set_mask(mask[abbreviation])
self.__add_tissue__(tis)
else:
assert isinstance(tissue, Tissue)
tissue.set_mask(mask)
self.__add_tissue__(tissue)
return mask