INSTITUT DE RECERCA EN VISIÓ PER COMPUTADOR I ROBÒTICA – vicorob. udg

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Presentation transcript:

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

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

CNN architecture (II) Electronical device has noise!

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

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

Qualitative results

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