Segmentation

Author : OpenTPS team

This example will present the basis of segmentation with openTPS core. running time: ~ 5 minutes

Setting up the environment in google collab

import sys
if "google.colab" in sys.modules:
    from IPython import get_ipython
    get_ipython().system('git clone https://gitlab.com/openmcsquare/opentps.git')
    get_ipython().system('pip install ./opentps')

    import opentps

imports

import numpy as np
import matplotlib.pyplot as plt
import logging
import os

import the needed opentps.core packages

from opentps.core.data.images import CTImage
from opentps.core.processing.segmentation.segmentation3D import applyThreshold
from opentps.core.processing.segmentation.segmentationCT import SegmentationCT
from opentps.core.data.images._ctImage import CTImage
from opentps.core.data.images._roiMask import ROIMask

logger = logging.getLogger(__name__)

Output path

output_path = 'Output'
if not os.path.exists(output_path):
    os.makedirs(output_path)

Genrerate synthetic CT image and segment it

def createSynthetic3DCT(diaphragmPos = 20, targetPos = [50, 100, 35], spacing=[1, 1, 2], returnTumorMask = False):
    # GENERATE SYNTHETIC CT IMAGE
    # background
    im = np.full((170, 170, 100), -1000)
    im[20:150, 70:130, :] = 0
    # left lung
    im[30:70, 80:120, diaphragmPos:] = -800
    # right lung
    im[100:140, 80:120, diaphragmPos:] = -800
    # target
    im[targetPos[0]-5:targetPos[0]+5, targetPos[1]-5:targetPos[1]+5, targetPos[2]-5:targetPos[2]+5] = 0
    # vertebral column
    im[80:90, 95:105, :] = 800
    # rib
    im[22:26, 90:110, 46:50] = 800
    # couch
    im[:, 130:135, :] = 100
    ct = CTImage(imageArray=im, name='fixed', origin=[0, 0, 0], spacing=spacing)

    if returnTumorMask:
        mask = np.full((170, 170, 100), 0)
        mask[targetPos[0]-5:targetPos[0]+5, targetPos[1]-5:targetPos[1]+5, targetPos[2]-5:targetPos[2]+5] = 1
        roi = ROIMask(imageArray=mask, origin=[0, 0, 0], spacing=spacing)

        return ct, roi

    else:
        return ct

# GENERATE SYNTHETIC CT IMAGE
ct = createSynthetic3DCT()

# APPLY THRESHOLD SEGMENTATION
mask = applyThreshold(ct, -750)

# APPLY CT BODY SEGMENTATION
seg = SegmentationCT(ct)
body = seg.segmentBody()
bones = seg.segmentBones()
lungs = seg.segmentLungs()

# CHECK RESULTS
assert (body.imageArray[50,100,80] == True) & (body.imageArray[0,0,0] == False), f"Wrong body segmentation"
assert (bones.imageArray[85,100,50] == True) & (bones.imageArray[85,110,50] == False), f"Wrong bones segmentation"
assert (lungs.imageArray[120,100,35] == True) & (lungs.imageArray[50,100,35] == False), f"Wrong lungs segmentation"

DISPLAY RESULTS

fig, ax = plt.subplots(2, 5)
fig.tight_layout()
y_slice = 100
z_slice = 35 #round(ct.imageArray.shape[2] / 2) - 1
ax[0,0].imshow(ct.imageArray[:, y_slice, :].T[::-1, ::1], cmap='gray', origin='upper', vmin=-1000, vmax=1000)
ax[0,0].title.set_text('CT')
ax[0,1].imshow(mask.imageArray[:, y_slice, :].T[::-1, ::1], cmap='gray', origin='upper', vmin=0, vmax=1)
ax[0,1].title.set_text('Threshold')
ax[0,2].imshow(body.imageArray[:, y_slice, :].T[::-1, ::1], cmap='gray', origin='upper', vmin=0, vmax=1)
ax[0,2].title.set_text('Body')
ax[0,3].imshow(bones.imageArray[:, y_slice, :].T[::-1, ::1], cmap='gray', origin='upper', vmin=0, vmax=1)
ax[0,3].title.set_text('Bones')
ax[0,4].imshow(lungs.imageArray[:, y_slice, :].T[::-1, ::1], cmap='gray', origin='upper', vmin=0, vmax=1)
ax[0,4].title.set_text('Lungs')

ax[1,0].imshow(ct.imageArray[:, :, z_slice].T[::1, ::1], cmap='gray', origin='upper', vmin=-1000, vmax=1000)
ax[1,0].title.set_text('CT')
ax[1,1].imshow(mask.imageArray[:, :, z_slice].T[::1, ::1], cmap='gray', origin='upper', vmin=0, vmax=1)
ax[1,1].title.set_text('Threshold')
ax[1,2].imshow(body.imageArray[:, :, z_slice].T[::1, ::1], cmap='gray', origin='upper', vmin=0, vmax=1)
ax[1,2].title.set_text('Body')
ax[1,3].imshow(bones.imageArray[:, :, z_slice].T[::1, ::1], cmap='gray', origin='upper', vmin=0, vmax=1)
ax[1,3].title.set_text('Bones')
ax[1,4].imshow(lungs.imageArray[:, :, z_slice].T[::1, ::1], cmap='gray', origin='upper', vmin=0, vmax=1)
ax[1,4].title.set_text('Lungs')

plt.savefig(os.path.join(output_path, 'Example_Segmentation.png'))

print('Segmentation example completed')
plt.show()

Segmentation example completed