Deep-Learning-with-PyTorch

Updating the dataset for segmentation
375
we could fix, but since we’re not yet convinced that’s the best use of our time and attention,
we’ll let it remain as is for now. 8 Next, we’ll go about adding this mask to our CT class.
CALLING MASK CREATION DURING CT INITIALIZATION
Now that we can take a list of nodule information tuples and turn them into at CTshaped
binary “Is this a nodule?” mask, let’s embed those masks into our CT object.
First, we’ll filter our candidates into a list containing only nodules, and then we’ll use
that list to build the annotation mask. Finally, we’ll collect the set of unique array
indexes that have at least one voxel of the nodule mask. We’ll use this to shape the
data we use for validation.
Listing 13.5
dsets.py:99, Ct.__init__
def __init__(self, series_uid):
# ... line 116
candidateInfo_list = getCandidateInfoDict()[self.series_uid]
Filters for
nodules
self.positiveInfo_list = [
candidate_tup
Gives us a 1D vector (over the
for candidate_tup in candidateInfo_list slices) with the number of voxels
if candidate_tup.isNodule_bool
flagged in the mask in each slice
]
self.positive_mask = self.buildAnnotationMask(self.positiveInfo_list)
self.positive_indexes = (self.positive_mask.sum(axis=(1,2))
.nonzero()[0].tolist())
Takes indices of the mask slices that have a
nonzero count, which we make into a list
Keen eyes might have noticed the getCandidateInfoDict function. The definition isn’t
surprising; it’s just a reformulation of the same information as in the getCandidate-
InfoList function, but pregrouped by series_uid.
Listing 13.6
This can be useful to
keep Ct init from being a
dsets.py:87
performance bottleneck.
@functools.lru_cache(1)
def getCandidateInfoDict(requireOnDisk_bool=True):
candidateInfo_list = getCandidateInfoList(requireOnDisk_bool)
candidateInfo_dict = {}
for candidateInfo_tup in candidateInfo_list:
candidateInfo_dict.setdefault(candidateInfo_tup.series_uid,
[]).append(candidateInfo_tup)
return candidateInfo_dict
Takes the list of candidates for the series UID
from the dict, defaulting to a fresh, empty list
if we cannot find it. Then appends the
present candidateInfo_tup to it.
8
Fixing this issue would not do a great deal to teach you about PyTorch.