Deep-Learning-with-PyTorch

Bridging CT segmentation and nodule candidate classification
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This was easy enough, but now we need to invent the grouping.
14.3.2 Grouping voxels into nodule candidates
We are going to use a simple connected-components algorithm for grouping our suspected
nodule voxels into chunks to feed into classification. This grouping approach
labels connected components, which we will accomplish using scipy.ndimage
.measurements.label. The label function will take all nonzero pixels that share an
edge with another nonzero pixel and mark them as belonging to the same group.
Since our output from the segmentation model has mostly blobs of highly adjacent
pixels, this approach matches our data well.
Listing 14.3
nodule_analysis.py:401
Assigns each voxel the label
of the group it belongs to
def groupSegmentationOutput(self, series_uid, ct, clean_a):
candidateLabel_a, candidate_count = measurements.label(clean_a)
centerIrc_list = measurements.center_of_mass(
ct.hu_a.clip(-1000, 1000) + 1001,
Gets the center of mass for
labels=candidateLabel_a,
each group as index, row,
index=np.arange(1, candidate_count+1),
column coordinates
)
The output array candidateLabel_a is the same shape as clean_a, which we used for
input, but it has 0 where the background voxels are, and increasing integer labels 1, 2,
…, with one number for each of the connected blobs of voxels making up a nodule candidate.
Note that the labels here are not the same as labels in a classification sense! These
are just saying “This blob of voxels is blob 1, this blob over here is blob 2, and so on.”
SciPy also sports a function to get the centers of mass of the nodule candidates:
scipy.ndimage.measurements.center_of_mass. It takes an array with per-voxel densities,
the integer labels from the label function we just called, and a list of which of
those labels need to have a center calculated. To match the function’s expectation
that the mass is non-negative, we offset the (clipped) ct.hu_a by 1,001. Note that this
leads to all flagged voxels carrying some weight, since we clamped the lowest air value
to –1,000 HU in the native CT units.
Listing 14.4
nodule_analysis.py:409
candidateInfo_list = []
for i, center_irc in enumerate(centerIrc_list):
center_xyz = irc2xyz(
center_irc,
ct.origin_xyz,
ct.vxSize_xyz,
ct.direction_a,
)
Converts the voxel
coordinates to real
patient coordinates