Interpret and explain your segmetation models through analysing their sensitivity to defined alterations of the input

Input alterations currently include:

  • rotation
  • cropping
  • brightness
  • contrast
  • zooming
  • flipping (dihedral)
  • resizing
  • MR artifacts (via torchio)

Install

pip install misas

Cite

If you use misas in your research, please cite:

Ankenbrand, M. J., Shainberg, L., Hock, M., Lohr, D., & Schreiber, L. M. Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI. BMC Medical Imaging, 21(27). https://doi.org/10.1186/s12880-021-00551-1

If you use the simulated MR artifacts, please also cite torchio:

F. Pérez-García, R. Sparks, and S. Ourselin. TorchIO:a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning. Computer Methods and Programs in Biomedicine (June 2021), p. 106236. ISSN: 0169-2607. https://doi.org/10.1016/j.cmpb.2021.106236

How to use

Example with kaggle data

from fastai.vision import *
from misas.core import default_cmap

img = lambda: open_image("example/kaggle/images/1-frame014-slice005.png")
trueMask = lambda: open_mask("example/kaggle/masks/1-frame014-slice005.png")
trainedModel = Fastai1_model('chfc-cmi/cmr-seg-tl', 'cmr_seg_base')
fig, ax = plt.subplots(figsize=(8,8))
img().show(ax=ax)
trueMask().show(ax=ax, cmap=default_cmap)

Rotation 

plot_series(get_rotation_series(img(), trainedModel))

results = eval_rotation_series(img(), trueMask(), trainedModel)
plt.plot(results['deg'], results['c1'])
plt.plot(results['deg'], results['c2'])
plt.axis([0,360,0,1])

(0.0, 360.0, 0.0, 1.0)

You can use interactive elements to manually explore the impact of rotation

from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets

rotation_series = get_rotation_series(img(),trainedModel,step=10)

def plot_rotation_frame(deg):
    return plot_frame(*rotation_series[int(deg/10)], figsize=(10,10))

interact(
    plot_rotation_frame,
    deg=widgets.IntSlider(min=0, max=360, step=10, value=90, continuous_update=False)
)

There are lots of other transformations to try (e.g. cropping, brightness, contrast, ...) as well as MR specific artifacts.

Overview

This is the schematic overview of how misas works. Created with the amazing Excalidraw. schema

The logo was designed by Markus J. Ankenbrand using:

Attribution

This project is inspired by the awesome "Is it a Duck or Rabbit" tweet by @minimaxir. Also check out the corresponding repo.

%%html
<blockquote class="twitter-tweet"><p lang="en" dir="ltr">Is it a Duck or a Rabbit? For Google Cloud Vision, it depends how the image is rotated. <a href="https://t.co/a30VzjEXVv">pic.twitter.com/a30VzjEXVv</a></p>&mdash; Max Woolf (@minimaxir) <a href="https://twitter.com/minimaxir/status/1103676561809539072?ref_src=twsrc%5Etfw">March 7, 2019</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>