1. INSTITUT DE RECERCA EN VISIÓ PER COMPUTADOR I ROBÒTICA – vicorob. udg
INSTITUT DE RECERCA EN VISIÓ PER COMPUTADOR I ROBÒTICA – vicorob.udg.edu
MULTIPLE SCLEROSIS LESION DETECTION AND SEGMENTATION USING A CONVOLUTIONAL NEURAL NETWORK OF 3D PATCHES
Sergi Valverde, Mariano Cabezas, Eloy Roura, Sandra González-Villà, Joaquim Salvi, Arnau Oliver and Xavier Lladó

2. CNN architecture (I)
Input: preprocessed FLAIR, T1, T2 and PD modalities
15 x 15 x 15 x 4 input
Convolutional 1: 32 x 7 x 7 x 7
Average pooling 1: size 2 and stride 2
Convolutional 2: 64 x 3 x 3 x 3
Average pooling 2: size 2 and stride 2
Dropout (t = 0.5)
Fully connected layer: 256 units
Soft-max layer: 2 units

3. CNN architecture (II)
Electronical device has noise!

4. Training Train CNN1 Train CNN2 15 training images Balanced dataset
All lesion voxels + same number of random sampled negatives
Train CNN1
Test 15 training images
Extract voxels incorrectly classified as lesions
Balanced dataset
All lesion voxels + same number of voxels incorrectly classified in CNN1
Train CNN2

5. Testing ✕ Test CNN1 Test CNN2 TEST image FLAIR, T1, T2, PD
Binarize mask
Apply a threshold on the multiplied probability mask ✕
FINAL SEGMENTATION
TEST image
FLAIR, T1, T2, PD
Test CNN2
Filter out
Remove regions with < 20 voxels

6. Qualitative results

7. INSTITUT DE RECERCA EN VISIÓ PER COMPUTADOR I ROBÒTICA – vicorob. udg
INSTITUT DE RECERCA EN VISIÓ PER COMPUTADOR I ROBÒTICA – vicorob.udg.edu
MULTIPLE SCLEROSIS LESION DETECTION AND SEGMENTATION USING A CONVOLUTIONAL NEURAL NETWORK OF 3D PATCHES
Sergi Valverde, Mariano Cabezas, Eloy Roura, Sandra González-Villà, Joaquim Salvi, Arnau Oliver and Xavier Lladó