MEDICAL IMAGE SEGMENTATION WITH DIGITS. Hyungon Ryu, Jack Han Solution Architect NVIDIA Corporation
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1 MEDICAL IMAGE SEGMENTATION WITH DIGITS Hyungon Ryu, Jack Han Solution Architect NVIDIA Corporation
2 MEDICAL IMAGE SEGMENTATION WITH DIGITS Overview Prepare Dataset Configure DL Model DL Training Segmentation
3 Segmentation
4 SEGMENTATION Pascal VOC 2012 dataset #0: background #1: aeroplane #2: bicycle #3: bird #4: boat #5: bottle #6: bus #7: car #8: cat #9: chair #10: cow #11: diningtable #12: dog #13: horse #14: motorbike #15: person #16: pottedplant #17: sheep #18: sofa #19: train #20: tvmonitor #255: undefined/don't care
5 SEGMENTATION MS COCO dataset
6 SEGMENTATION ITK-SNAP Kidney-Liver Segmentation Brain Segmentation
7 SEGMENTATION Medical Image Cancer Cell Vessel Segmentation
8 Dataset
9 CARDIAC MR LEFT VENTRICLE SEGMENTATION MIDAS Journal Cardiac MR Left Ventricle Segmentation Challenge
10 SLICE VIEW from wikipedia
11 DICOM VIEWER ezdicom
12 DATASET WITH CONTOUR X Y
13 DATASET WITH CONTOUR
14 24 LAB
15 DIGITS PLUGINS DIGITS Plugins Image : Sunnybrook LV Segmentation plugins/data/sunnybrook DIGITS Plugins Image : Regression plugins/data/imagegradients DIGITS Plugins Text plugins/data/textclassification
16 PREPARE DATA
17 DATASET
18 Image cantour X Y
19 CONFIGURE DL MODEL
20 CONFIGURE DL MODEL
21 VISUALIZE
22 MONITOR TRAIN
23 TEST
24 REASON Cine MR Same Object, time variance
25 PRACTICE 2 DICE METRIC
26 DICE METRIC intersection Union Manual contour (Expert) Automatic (DL)
27 DICE METRIC
28 PYTHON LAYER import random import numpy as np import caffe class Dice(caffe.Layer): """ A layer that calculates the Dice coefficient """ def setup(self, bottom, top): if len(bottom)!= 2: raise Exception("Need two inputs to compute Dice coefficient.") def reshape(self, bottom, top): # check input dimensions match if bottom[0].count!= 2*bottom[1].count: raise Exception("Prediction must have twice the number of elements of the input.") # loss output is scalar top[0].reshape(1) def forward(self, bottom, top): #print "bottom[0].shape=%s" % repr(bottom[0].data.shape) #print "bottom[1].shape=%s" % repr(bottom[1].data.shape) label = bottom[1].data[:,0,:,:] # compute prediction prediction = np.argmax(bottom[0].data, axis=1) #print "prediction.shape=%s" % repr(prediction.shape) # area of predicted contour a_p = np.count_nonzero(prediction) # area of contour in label a_l = np.count_nonzero(label) # area of intersection a_pl = np.count_nonzero(prediction * label) #print "a_p=%f a_l=%f a_pl=%f" % (a_p, a_l, a_pl) # dice coefficient dice_coeff = 2.*a_pl/(a_p + a_l) top[0].data[...] = dice_coeff def backward(self, top, propagate_down, bottom): pass
29 PYTHON LAYER Type : Python
30 ENABLE DICE LAYER
31 DL TRAINING WITH DICE
32 RESULT2 WITH DICE
33 PRACTICE 3 USE PRE-TRAINED PARAMETERS
34 FOR ALEXNET (COLOR)
35 CONFIGURE DL MODEL WITH PRE-TRAINED
36 DICE(VAL)
37 RESULT WITH PRE-TRAIN
38 PRACTICE 4 MORE FINE DL MODEL
39 FCN-8S Alexnet VGG Resnet googlnet inception
40 DIFF VGG style Alexnet
41 Alxenet VGG style
42
43 RESULT
44 MEDICAL IMAGE SEGMENTATION WITH DIGITS summary Prepare Dataset Configure DL Model DL Training Segmentation With python layer(dice metric)
45 WHAT S NEXT TAKE SURVEY ACCESS ONLINE LABS Check your to access more DLI training online. ATTEND WORKSHOP Visit for workshops in your area. JOIN DEVELOPER PROGRAM Visit for more.
46
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