In [9]:

    

from __future__ import division, print_function

import os, glob
import numpy as np
import matplotlib.pyplot as plt

from rvseg import patient

%matplotlib inline


    

In [10]:

    

basedir = "/home/paperspace/Developer/datasets/rvsc/TrainingSet"
datadir = os.path.join(basedir, "patient09")
p = patient.PatientData(datadir)


    

In [14]:

    

for index in range(len(p.images)):
    plt.figure(figsize=(10,5))
    plt.subplot(1, 2, 1)
    plt.axis("off")
    plt.imshow(p.images[index], cmap=plt.cm.gray)
    plt.subplot(1, 2, 2)
    plt.axis("off")
    plt.imshow(p.images[index], cmap=plt.cm.gray)
    plt.plot(*p.endocardium_contours[index], color='springgreen')
    plt.plot(*p.epicardium_contours[index], color='orchid')


    

In [ ]:

    

from scipy import interpolate


    

In [31]:

    

dices = []

stride = 40
for n in range(len(p.images)):
    image = p.images[n]
    x, y = p.endocardium_contours[n]
    x1 = np.concatenate((x[::stride], [x[n]]))
    y1 = np.concatenate((y[::stride], [y[n]]))
    tck, u = interpolate.splprep((x1, y1), s=0)
    unew = np.arange(0, 1.01, 0.01)
    x2, y2 = interpolate.splev(unew, tck)
    mask1 = p.endocardium_masks[n]
    mask2 = p.contour_to_mask(x2, y2, norm=1)
    dice = 2 * np.sum(mask1 * mask2) / (np.sum(mask1) + np.sum(mask2))
    dices.append(dice)
    print(len(x), dice)
    plt.figure(figsize=(10,5))
    plt.subplot(1, 2, 1)
    plt.axis("off")
    plt.imshow(image, cmap=plt.cm.gray)
    plt.subplot(1, 2, 2)
    plt.axis("off")
    plt.imshow(image, cmap=plt.cm.gray)
    plt.plot(x, y)
    plt.plot(x2, y2)
print(np.mean(dices), np.std(dices))


    

    




467 0.967144522944
448 0.959858156028
458 0.962323221419
434 0.955210402745
472 0.973527307417
457 0.970275309861
452 0.969300160047
440 0.940730079812
452 0.947448882094
425 0.951979234263
439 0.958416977344
413 0.971760154739
463 0.964255319149
440 0.957437472576
404 0.952908587258
396 0.959245742092
398 0.954940711462
0.959809543603 0.00870759062761






    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    

In [36]:

    

base = "/home/paperspace/Developer/work/rvsc/"

paths = [
    base + "aaug/1e-3/unet-aaug",
    base + "mnt/dilated-unet/aaug/1e-3/dilated-unet-aaug",
    base + "dilated-densenet/aaug/8-layer-24-features/dilated-densenet-8-24-aaug"
]

paths = [
    base + "eaug/1e-3/unet-eaug",
    base + "mnt/dilated-unet/eaug/1e-3/dilated-unet-eaug",
    base + "dilated-densenet/eaug/8-layer-24-features/dilated-densenet-8-layer-24-features"
]


DICE, JACCARD = 0, 1
train = []
val = []
for path in paths:
    train.append(np.loadtxt(path + ".train").T[DICE])
    val.append(np.loadtxt(path + ".val").T[DICE])

plt.figure(figsize=(10,5))
plt.subplot(1, 2, 1)
plt.boxplot(train)
plt.ylim(-0.03, 1.03)
plt.ylabel("training dice coefficient")
plt.xticks([1, 2, 3], ["u-net", "dilated u-net", "dilated densenet"])
plt.subplot(1, 2, 2)
plt.boxplot(val)
plt.ylim(-0.03, 1.03)
plt.ylabel("validation dice coefficient")
plt.xticks([1, 2, 3], ["u-net", "dilated u-net", "dilated densenet"])


    

    
Out[36]:





([<matplotlib.axis.XTick at 0x7f52368ac908>,
  <matplotlib.axis.XTick at 0x7f52368acf60>,
  <matplotlib.axis.XTick at 0x7f5236896470>],
 <a list of 3 Text xticklabel objects>)