Deep-Learning-with-PyTorch

Updating the dataset for segmentation
377
Consortium image collection (LIDC-IDRI) 9 and includes detailed annotation
information from multiple radiologists. We’ve already done the legwork to get the
original LIDC annotations, pull out the nodules, dedupe them, and save them to the
file /data/part2/luna/annotations_with_malignancy.csv.
With that file, we can update our getCandidateInfoList function to pull our nodules
from our new annotations file. First, we loop over the new annotations for the
actual nodules. Using the CSV reader, 10 we need to convert the data to the appropriate
types before we stick them into our CandidateInfoTuple data structure.
Listing 13.9
dsets.py:43, def getCandidateInfoList
candidateInfo_list = []
with open('data/part2/luna/annotations_with_malignancy.csv', "r") as f:
for row in list(csv.reader(f))[1:]:
series_uid = row[0]
annotationCenter_xyz = tuple([float(x) for x in row[1:4]])
annotationDiameter_mm = float(row[4])
isMal_bool = {'False': False, 'True': True}[row[5]]
For each line in
the annotations
file that
represents one
nodule, …
candidateInfo_list.append(
CandidateInfoTuple(
True,
True,
isMal_bool,
annotationDiameter_mm,
series_uid,
annotationCenter_xyz,
)
)
isNodule_bool
… we add a record to our list.
hasAnnotation_bool
Similarly, we loop over candidates from candidates.csv as before, but this time we only
use the non-nodules. As these are not nodules, the nodule-specific information will
just be filled with False and 0.
Listing 13.10
dsets.py:62, def getCandidateInfoList
with open('data/part2/luna/candidates.csv', "r") as f:
for row in list(csv.reader(f))[1:]:
series_uid = row[0]
# ... line 72
if not isNodule_bool:
candidateInfo_list.append(
CandidateInfoTuple(
For each line in the
candidates file …
… but only the non-nodules (we
have the others from earlier) …
… we add a candidate record.
9
Samuel G. Armato 3rd et al., 2011, “The Lung Image Database Consortium (LIDC) and Image Database
Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans,” Medical Physics
38, no. 2 (2011): 915-31, https://pubmed.ncbi.nlm.nih.gov/21452728/. See also Bruce Vendt, LIDC-IDRI,
Cancer Imaging Archive, http://mng.bz/mBO4.
10 If you do this a lot, the pandas library that just released 1.0 in 2020 is a great tool to make this faster. We stick
with the CSV reader included in the standard Python distribution here.