MICCAI 2020 Tutorial

Weakly Supervised CNN Segmentation: Models and Optimization

This repository contains the code of the hand-on tutorial, that runs on two datasets:

• A very simple toy example
• PROMISE12 prostate segmentation challenge

Also founds, the slides and recordings of the tutorial.

Slides

Slides from the three sessions are available in the slides/ folder.

Recordings

• Session 1: Structure-driven priors: Regularization
• Session 2: Knowledge-driven priors (e.g., anatomy): Constraints
• Session 3: Data-driven priors: Adversarial learning
• Session 4: Hands-on: Size constraints

Hands-on

The goal here is to enforce some inequality constraints on the size of the predicted segmentation in the form:

lower bound <= predicted size <= upper bound

where predicted size is the sum of all predicted probabilities (softmax) over the whole image.

To make the example simpler, we will define the lower and upper bounds to 0.9 and 1.1 times the ground truth size. All the code is contained within the code folder

Requirements

The code has those following dependencies:

python3.7+
pytorch (latest)
torchvision
numpy
tqdm

Running the PROMISE12 example requires some additional packages:

simpleitk
scikit-image
PIL

Data

The data for the toy example is stored in code/data/TOY. If you wish, you can regenerate the dataset with:

make -B data/TOY

or you can use gen_toy.py directly.

Participants willing to try the PROMISE12 setting need to download the data themselves, then put the .zip inside the code/data folder (a list of files is available in code/data/promise12.lineage). Once the three files are there, the slicing into 2D png files is automated:

make data/PROMISE12

It will:

• checks data integrity
• extract the zip
• slice into 2d slices
• generate weak labels from the actual ground truth

Training

>>> ./main.py -h
usage: main.py [-h] [--epochs EPOCHS] [--dataset {TOY,PROMISE12}] [--mode {constrained,unconstrained,full}] [--gpu]

optional arguments:
  -h, --help            show this help message and exit
  --epochs EPOCHS
  --dataset {TOY,PROMISE12}
  --mode {constrained,unconstrained,full}
  --gpu

The toy example is designed to run under 5 minutes on a laptop, training on CPU. The following commands are equivalent

python3 main.py
./main.py
./main.py --epochs 200 --dataset TOY --mode unconstrained

The three modes correspond to:

• unconstrained: use the weak labels, with only a partial cross-entropy (won't learn anything)
• constrained: use the weak labels, with partial cross-entropy + size constraint (will learn)
• full: use full labels, with cross entropy (will learn, for obvious reasons)

The settings for PROMISE12 are too simple to get state of the art results, even in the full mode, but it gives a good starting point for new practitioners to then build on.