superiq

super-resolution image quantitation

make super-resolution fly: quantitatively and at scale

quantitative methods for super-resolution images

• task-specific super-resolution (SR)

  • global tissue segmentation with priors
  • local cortical segmentation
  • hippocampus segmentation
  • basal forebrain segmentation
  • deep brain structure segmentation
    • substantia nigra
    • caudate, putamen, etc

• general purpose methodology:

  • simultaneous SR for intensity and segmentation probability pairs
  • super-resolution multi-atlas segmentation (SRMAS)
  • local joint label fusion for arbitrary segmentation libraries
  • local joint label fusion for single templates with augmentation

tests provide a good example of use cases.

• install: python setup.py install

• test: python tests/test_segmentation.py

Regions

Eisai regions

• group 1: basal forebrain 75L, 76R

• group2: hippocampus

  • use deep hippocampus but 48L, 47R

• group 3: cortex

  • entorhinal: 117L, 116R
  • parahippocampal: 171L 170R
  • middle temporal gyrus: 155L 154R
  • fusiform gyrus: 123L 122R

PD regions for MJFF+PPMI

• total intracranial volume (ICV),
• caudate, => 37L, 36R
• putamen => 58L, 57R
• substantia nigra (SN) => is within 62L and 61R but should be dealt with separately
• globus pallidus => 56L, 55R
• corticospinal tract => TODO via registration from template
• supplementary motor area ( 183=left, 182=right - actually a subset of this but misses medial portion )
• wlab = [36,37,57,58,61,62,55,56,183,182]

TODO

• documentation

• testing

  • figure out how to distribute sr models

• ....