mistycheney

// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

mistycheney

import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
plt.figure()
ax = plt.gca()
im = ax.imshow(np.arange(100).reshape((10,10)))
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
plt.colorbar(im, cax=cax)

mistycheney

# Configuration file for ipython-notebook.

c = get_config()

#------------------------------------------------------------------------------
# NotebookApp configuration
#------------------------------------------------------------------------------

# NotebookApp will inherit config from: BaseIPythonApplication, Application

mistycheney

stack = 'MD589'
data_dir = '/oasis/projects/nsf/csd395/yuncong/CSHL_data_patches/%s_byLandmark'%(stack)

all_image_names = []
for label in os.listdir(data_dir):
    if label == 'BackG':
        # limit the number of BackG examples
        for img_filename in os.listdir(data_dir + '/' + label)[:300]:
            all_image_names.append((label_dict[label], label + '/' + img_filename))
    else:            

mistycheney

def less(center):
    def less_helper(a, b):
        if (a[0] - center[0] >= 0 and b[0] - center[0] < 0):
            return 1;
        if (a[0] - center[0] < 0 and b[0] - center[0] >= 0):
            return -1;
        if (a[0] - center[0] == 0 and b[0] - center[0] == 0):
            if (a[1] - center[1] >= 0 or b[1] - center[1] >= 0):
                return 2*int(a[1] > b[1]) - 1;
            return 2*int(b[1] > a[1]) - 1

mistycheney

# https://gist.github.com/hellpanderrr/2c08af0f07eed4782234

from scipy.spatial import Delaunay, ConvexHull
import networkx as nx

def concave_hull(points,alpha_x=150,alpha_y=250):
    points = [(i[0],i[1]) if type(i) <> tuple else i for i in points]
    de = Delaunay(points)
    dec = []
    a = alpha_x

mistycheney

# http://stackoverflow.com/a/31402351

def bbox_2d(img):
    rows = np.any(img, axis=1)
    cols = np.any(img, axis=0)
    rmin, rmax = np.where(rows)[0][[0, -1]]
    cmin, cmax = np.where(cols)[0][[0, -1]]

    return cmin, cmax, rmin, rmax

mistycheney

Incorporating Uncertainty

If the score maps given by the classifier are not extremely reliable, we need to balance between the reference structure locations constrained by the atlas and the evidence from score maps.

We do this in a Bayesian setting. Suppose the atlas gives a prior distribution for the structure's location, represented by $P(\theta)$. An observation distribution $P(s | \Omega)$ models classification uncertainty, where $\Omega$ is the set of voxels that belong to a particular structure, and $s$ is the computed score volume.

Let $\Omega(\theta)$ denote this structure's voxel locations transformed by $\theta$, and let $s$ denote the score volume, then the likelihood of the score volume is $P(s | \Omega(\theta))$. The optimal transform $\theta^*$ should maximize the posterior:

$$ \theta^* = \arg\max P(s | \Omega(\theta)) P(\theta).$$

mistycheney

mistycheney

// Batch Convert
//
// This macro convert all the files in a folder to TIFF, 8-bit TIFF, 
// JPEG, GIF, PNG, PGM, BMP, FITS, Text Image, ZIP or Raw
// format. Three dialog boxes are displayed. Select the source 
// folder in the first, the format in the second and the destination 
// folder in the third. Batch_Converter, a similar plugin is at 
//    http://rsb.info.nih.gov/ij/plugins/batch-converter.html
// https://imagej.nih.gov/ij/plugins/batch-converter.html