1. Journal Club M Havaei, et al. Université de Sherbrooke, Canada
“Deep learning trends for focal brain pathology segmentation in MRI”
Feb 3, 2017
Jason Su

2. BRATS – Brain Tumor Segmentation
T1, T1C, T2, FLAIR
BRATS 2012, 3 classes: healthy, edema and core
10/20 low/high grade glioma for training
5/11 for testing
BRATS 2013 separated into 5 classes: healthy, necrosis, edema, non-enhanced, enhanced tumor
BRATS 2015, 5 classes
54/220 for training, truth generated by previous years’ algorithms
53 images for testing
Performance measures: dice, sensitivity, specificity, kappa, Hausdorff distance

3. ISLES – Ischemic Stroke Lesion Seg.
Started in 2015
SISS sub-acute dataset
FLAIR, DWI, T2 TSE, T1 TFE
28 training, 36 testing
SPES acute stroke
Cerebral blood flow, cerebral blood volume, DWI, T1C, T2, Tmax, time to peak
30 training, 20 testing
Performance measures: dice, average symmetric surface distance, Hausdorff distance, recall, precision

4. MSGC – Multiple Sclerosis
Started in 2008
T1, T2, FLAIR
20 training, 23 testing from CHB and UNC
Performance metrics: volume difference, surface distance, true positive rate, false positive rate

5. Old Techniques Semi-automatic Automatic
Initialize tumor contour, seed regions
Automatic
Anomaly detection by comparison to healthy atlas (MSmcDESPOT, multi-atlas segmentation)
Machine learning
A discriminative model trained on pre-defined/hand-crafted features
Random forests popular (best out-of-the-box algo.)
Gaussian mixture models
Markov and conditional random fields for regularization of kNN or random forests

6. Deep Learning Increasingly being used in and winning MICCAI challenges
Most methods are 2D, slice by slice
A typical classification CNN has many layers that aggregate data to understand the global picture
However, segmentation is both a global and local task

7. TwoPathCNN – Havaei et al.
Patchwise training, given a patch around a central voxel, classify the voxel
“Global” and “local” pathways
Top 4 in BRATS 2015
Fully convolutional, no fully connected layers

8. “Deconvolution” or Transposed Conv.
Want to swap the input and output dimensions

9. Convolutional Encoder Network
MS lesion segmentation
Encoder and decoder
Encoder in valid mode
Decoder in full mode
Input patch -> output patch
Instead of patch to one output voxel
9x9x9

10. U-Net Current state of the art Influenced by the previous
Very popular in MICCAI 2016
Works well with low data
Influenced by the previous
Up-conv 2x2 = bilinear up-sampling then 2x2 convolution
2D slices
3x3 convolutions

11. Misc Adaptations Multi-modality 3D
Instead of treating these as channels, process each in its own convolutional network and merge 3D
Hard due to different contrasts’ resolutions
2.5D (orthogonal slices) is cheaper but no clear gain
Some have done true 3D
3D version of TwoPathCNN was successful in ISLES 2015

12. Training Issues Normalization
Scaling all contrasts or vendors to the same range
N3/N4 bias field or scale by CSF
Truncate outliers, 1% or 0.1% quantiles
Histogram normalization
Zero mean, unit variance
Shuffle input patches so doesn’t overtrain on one subject
Unbalanced classes
Sample evenly
Weight loss for different classes by proportion
Importance sampling

13. Other Issues Structured prediction Limited data
Patches are treated independently but shouldn’t be
Model spatial dependencies
Implicit, aggregate outputs through a cascaded CNN
Explicit, incorporate pairwise term into loss function or smooth outputs
Limited data
Data augmentation
Flips, rotations
Nonlinear deformation
Transfer learning
Adapt input to 3 channels in some manner (2.5D, HDR)
Train on grayscale ImageNet
Unsupervised pre-training
Autoencoders

14. Other Issues Missing data
Impute missing contrast with 0, the mean, or estimate from other contrasts
Voting strategy
Learning robust representations which are invariant to the noise introduced by the acquisition is needed