Tube segmentation¶
[1]:
from brainlit.utils import session
from brainlit.feature_extraction import *
import napari
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from .autonotebook import tqdm as notebook_tqdm
[2]:
url = "s3://open-neurodata/brainlit/brain1"
sess = session.NeuroglancerSession(url=url, url_segments=url+"_segments", mip=0)
SEGLIST=[101,103,106,107,109,11,111,112,115,11,12,120,124,126,127,129,13,132,133,136,137,14,140,141,142,143,144,145,146,147,149,150]
SEGLIST = SEGLIST[:1]
[3]:
# %%capture
nbr = NeighborhoodFeatures(url=url, radius=1, offset=[50,50,50], segment_url=url+"_segments")
nbr.fit(seg_ids=SEGLIST, num_verts=10, file_path='demo', batch_size=10)
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table = Table.from_pandas(df, preserve_index=preserve_index)
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table = Table.from_pandas(df, preserve_index=preserve_index)
[4]:
import glob, feather
feathers = glob.glob('*.feather')
for count, feather_file in enumerate(feathers):
if count == 0:
data = feather.read_dataframe(feather_file)
else:
df = feather.read_dataframe(feather_file)
data = pd.concat([data, df])
data.shape
[4]:
(20, 30)
[5]:
data.head()
[5]:
| Segment | Vertex | Label | 0 | 1 | 2 | 3 | 4 | 5 | 6 | ... | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 101 | 0 | 1 | 28778 | 30111 | 30120 | 28438 | 29909 | 30092 | 28315 | ... | 28751 | 30383 | 30683 | 29420 | 29565 | 30769 | 29762 | 28865 | 29364 | 29687 |
| 1 | 101 | 0 | 0 | 12290 | 12090 | 12222 | 12340 | 12215 | 12376 | 12185 | ... | 12208 | 12144 | 11845 | 12030 | 12682 | 12340 | 12336 | 12194 | 12383 | 12067 |
| 2 | 101 | 1 | 1 | 15429 | 16558 | 17587 | 15353 | 16662 | 17459 | 15462 | ... | 18105 | 14926 | 16441 | 17088 | 15187 | 16712 | 17474 | 15401 | 16939 | 18366 |
| 3 | 101 | 1 | 0 | 12166 | 12290 | 12180 | 12162 | 12129 | 12124 | 12058 | ... | 12297 | 12090 | 12038 | 12144 | 11959 | 11929 | 12116 | 12093 | 12298 | 12407 |
| 4 | 101 | 2 | 1 | 13005 | 12535 | 12333 | 12903 | 12660 | 12402 | 12846 | ... | 12502 | 12388 | 12404 | 12471 | 12860 | 12526 | 12441 | 12951 | 12771 | 12571 |
5 rows × 30 columns
[6]:
from sklearn.preprocessing import StandardScaler
X = data.iloc[:, 3:]
X = StandardScaler().fit_transform(X)
y = data["Label"]
[7]:
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=1)
clf = MLPClassifier(hidden_layer_sizes=4, activation="logistic", alpha=1, max_iter=1000).fit(X_train, y_train)
y_score = clf.predict_proba(X_test)
[8]:
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc
fpr, tpr, _ = roc_curve(y_test, y_score[:,1])
roc_auc = auc(fpr, tpr)
plt.figure()
lw = 2
plt.plot(fpr, tpr, color='darkorange',
lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('MLP ROC')
plt.legend(loc="lower right")
plt.show()
[9]:
from brainlit.feature_extraction.neighborhood import subsample
[10]:
X.shape
[10]:
(20, 27)
[11]:
from sklearn.linear_model import LogisticRegression
Xc_train, Xc_test, yc_train, yc_test = train_test_split(X, y, stratify=y, random_state=1)
clf = LogisticRegression(random_state=1, max_iter=2000).fit(Xc_train, yc_train)
yc_score = clf.predict_proba(Xc_test)
[12]:
fpr_c, tpr_c, _ = roc_curve(yc_test, yc_score[:,1])
roc_auc_c = auc(fpr_c, tpr_c)
plt.figure()
lw = 2
plt.plot(fpr_c, tpr_c, color='darkorange',
lw=lw, label='ROC curve (area = %0.2f)' % roc_auc_c)
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('LogRegression Receiver operating characteristic')
plt.legend(loc="lower right")
plt.show()
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