Deep-Learning-with-PyTorch

Updating the dataset for segmentation
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convolutions, in order to use information across slices. The memory usage of a straightforward
implementation would be considerably greater: that is, we would have to chop
up the CT scan. Also, the fact that pixel spacing in the Z direction is much larger than
in-plane makes a nodule less likely to be present across many slices. These considerations
make a fully 3D approach less attractive for our purposes. Instead, we’ll adapt
our 3D data to be segmented a slice at a time, providing adjacent slices for context (for
example, detecting that a bright lump is indeed a blood vessel gets much easier alongside
neighboring slices). Since we’re sticking with presenting the data in 2D, we’ll use
channels to represent the adjacent slices. Our treatment of the third dimension is similar
to how we applied a fully connected model to images in chapter 7: the model will
have to relearn the adjacency relationships we’re throwing away along the axial direction,
but that’s not difficult for the model to accomplish, especially with the limited
number of slices given for context owing to the small size of the target structures.
13.5 Updating the dataset for segmentation
Our source data for this chapter remains unchanged: we’re consuming CT scans and
annotation data about them. But our model expects input and will produce output of
a different form than we had previously. As we hint at in step 2B of figure 13.9, our
previous dataset produced 3D data, but we need to produce 2D data now.
The original U-Net implementation did not use padded convolutions, which
means while the output segmentation map was smaller than the input, every pixel of
that output had a fully populated receptive field. None of the input pixels that fed
1. Segmentation
UNet
2. Update:
2a. Model
T/F
2b. Dataset
t
3. Results
2c. Training
Figure 13.9 The outline of this chapter, with a focus on the changes needed for our
segmentation dataset