Deep-Learning-with-PyTorch

Updating the dataset for segmentation
373
We’ll skip showing the search in the third dimension. Our final bounding box is
five voxels wide and seven voxels tall. Here’s what that looks like in code, for the index
direction.
Listing 13.3
dsets.py:131, Ct.buildAnnotationMask
center_irc = xyz2irc(
candidateInfo_tup.center_xyz,
self.origin_xyz,
self.vxSize_xyz,
self.direction_a,
)
ci = int(center_irc.index)
cr = int(center_irc.row)
cc = int(center_irc.col)
candidateInfo_tup here is the same as
we’ve seen previously: as returned by
getCandidateInfoList.
Gets the center voxel
indices, our starting point
index_radius = 2
try:
while self.hu_a[ci + index_radius, cr, cc] > threshold_hu and \
self.hu_a[ci - index_radius, cr, cc] > threshold_hu:
index_radius += 1
except IndexError:
index_radius -= 1
The safety net for indexing
beyond the size of the tensor
The search
described
previously
We first grab the center data and then do the search in a while loop. As a slight complication,
our search might fall off the boundary of our tensor. We are not terribly
concerned about that case and are lazy, so we just catch the index exception. 7
Note that we stop incrementing the very approximate radius values after the density
drops below threshold, so our bounding box should contain a one-voxel border of lowdensity
tissue (at least on one side; since nodules can be adjacent to regions like the lung
wall, we have to stop searching in both directions when we hit air on either side). Since
we check both center_index + index_radius and center_index - index_radius
against that threshold, that one-voxel boundary will only exist on the edge closest to our
nodule location. This is why we need those locations to be relatively centered. Since
some nodules are adjacent to the boundary between the lung and denser tissue like muscle
or bone, we can’t trace each direction independently, as some edges would end up
incredibly far away from the actual nodule.
We then repeat the same radius-expansion process with row_radius and col
_radius (this code is omitted for brevity). Once that’s done, we can set a box in our
bounding-box mask array to True (we’ll see the definition of boundingBox_ary in just
a moment; it’s not surprising).
OK, let’s wrap all this up in a function. We loop over all nodules. For each nodule,
we perform the search shown earlier (which we elide from listing 13.4). Then, in a
Boolean tensor boundingBox_a, we mark the bounding box we found.
7
The bug here is that the wraparound at 0 will go undetected. It does not matter much to us. As an exercise,
implement proper bounds checking.