Deep-Learning-with-PyTorch

262 CHAPTER 10 Combining data sources into a unified dataset
Listing 10.5
candidateInfo_list.sort(reverse=True)
return candidateInfo_list
The ordering of the tuple members in noduleInfo_list is driven by this sort. We’re
using this sorting approach to help ensure that when we take a slice of the data, that
slice gets a representative chunk of the actual nodules with a good spread of nodule
diameters. We’ll discuss this more in section 10.5.3.
10.3 Loading individual CT scans
dsets.py:80, def getCandidateInfoList
This means we have all of the actual nodule
samples starting with the largest first, followed
by all of the non-nodule samples (which don’t
have nodule size information).
Next up, we need to be able to take our CT data from a pile of bits on disk and turn it
into a Python object from which we can extract 3D nodule density data. We can see this
path from the .mhd and .raw files to Ct objects in figure 10.4. Our nodule annotation
information acts like a map to the interesting parts of our raw data. Before we can follow
that map to our data of interest, we need to get the data into an addressable form.
TIP Having a large amount of raw data, most of which is uninteresting, is a
common situation; look for ways to limit your scope to only the relevant data
when working on your own projects.
CT Files
.MHD
.RAW
ANnotations
.CSV
CT
ARray
xyz2irc
Transform
1000
0 0
0 1 0 0
0 0 1 0
0001
0 0 Candidate
at
COordinate
(X,Y,Z)
Training
candidate
location
[(I,R,C),
(I,R,C),
(I,R,C),
...
]
Sample tuple
sample aRrayay
( ,
is nodule?
T/F,
Series_uid
“1.2.3”,
candidate
location
(I,R,C))
Figure 10.4 Loading a CT scan produces a voxel array and a transformation from
patient coordinates to array indices.