Deep-Learning-with-PyTorch

374 CHAPTER 13 Using segmentation to find suspected nodules
After the loop, we do a bit of cleanup by taking the intersection between the
bounding-box mask and the tissue that’s denser than our threshold of –700 HU (or 0.3
g/cc). That’s going to clip off the corners of our boxes (at least, the ones not embedded
in the lung wall), and make it conform to the contours of the nodule a bit better.
Listing 13.4
dsets.py:127, Ct.buildAnnotationMask
Starts with an all-False tensor
of the same size as the CT
Restricts
the mask to
voxels above
our density
threshold
def buildAnnotationMask(self, positiveInfo_list, threshold_hu = -700):
boundingBox_a = np.zeros_like(self.hu_a, dtype=np.bool)
for candidateInfo_tup in positiveInfo_list:
# ... line 169
boundingBox_a[
ci - index_radius: ci + index_radius + 1,
cr - row_radius: cr + row_radius + 1,
cc - col_radius: cc + col_radius + 1] = True
mask_a = boundingBox_a & (self.hu_a > threshold_hu)
return mask_a
Loops over the nodules. As a reminder
that we are only looking at nodules,
we call the variable positiveInfo_list.
After we get the nodule
radius (the search itself
is left out), we mark
the bounding box.
Let’s take a look at figure 13.11 to see what these masks look like in practice. Additional
images in full color can be found in the p2ch13_explore_data.ipynb notebook.
The bottom-right nodule mask demonstrates a limitation of our rectangular
bounding-box approach by including a portion of the lung wall. It’s certainly something
0
positive mask
100
200
300
400
500
0 100 200 300 400 500
Figure 13.11 Three nodules from
ct.positive_mask, highlighted
in white