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from dataprocesser.customized_transforms import create_body_contour, create_body_contour_by_seg_tissue
from dataprocesser.Preprocess_MR_Mask_generation import process_segmentation
import numpy as np
import pandas as pd
import nibabel as nib
def _create_body_contour(x, body_threshold, body_mask_value):
mask = np.zeros_like(x)
# [H, W, D]
# create a mask for each slice in the batch
for j in range(x.shape[-1]):
mask_slice = create_body_contour(x[:,:,j], body_threshold=body_threshold)
mask[:,:, j] = mask_slice
mask[mask == 1] = body_mask_value
return mask
def _create_body_contour_by_tissue_seg(x, body_mask_value=1, area_threshold=1000):
mask = np.zeros_like(x)
# [H, W, D]
# create a mask for each slice in the batch
for j in range(x.shape[-1]):
mask_slice = create_body_contour_by_seg_tissue(x[:,:,j], area_threshold=area_threshold)
mask[:,:, j] = mask_slice
mask[mask == 1] = body_mask_value
return mask
def _merge_seg_tissue(seg, tissue):
# because tissue seg from totalsegmentator is 1, 2, 3
# to avoid ambiguity
tissue[tissue > 0] += 200 # 1,2,3 + 200 = 201,202,203
# Create a mask for overlapping areas
overlap_mask = (seg > 0) & (tissue > 0)
# For overlapping areas, keep the lower value (organ values in seg)
merged_mask = tissue.copy()
merged_mask[overlap_mask] = seg[overlap_mask]
# Keep all non-overlapping areas
merged_mask[seg > 0] = seg[seg > 0]
return merged_mask
def _merge_seg_contour(seg, contour):
merged_mask = seg + contour
return merged_mask
def _assign_value_ct(csv_file, mask, key_id='Order Number', key_hu='HU Value'):
print('assign hu value to ct image prior')
df = pd.read_csv(csv_file)
hu_values = dict(zip(df[key_id], df[key_hu]))
order_begin_from_0 = True if df[key_id].min()==0 else False
hu_mask = np.zeros_like(mask)
# Value Assigment
hu_mask[mask == 0] = -1000 # background
for organ_index, hu_value in hu_values.items():
assert isinstance(hu_value, int), f"Expected mask value an integer, but got {hu_value}. Ensure the mask is created by fine mode of totalsegmentator"
assert isinstance(organ_index, int), f"Expected organ_index an integer, but got {organ_index}. Ensure the mask is created by fine mode of totalsegmentator"
if order_begin_from_0:
#print("order in csv begin from 0")
hu_mask[mask == (organ_index+1)] = hu_value # mask value begin from 1 as body value, other than 0 in TA2 table, so organ_index+1
else:
#print("order in csv begin from 1")
hu_mask[mask == (organ_index)] = hu_value
return hu_mask
def _assign_value_mr(csv_file, mask, mr_signal_formula):
csv_simulation_values = pd.read_csv(csv_file, header=None).to_numpy()
assign_value_mask = process_segmentation(mask, csv_simulation_values, mr_signal_formula)
return assign_value_mask |