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duke_ultrasound

  • Description:

DukeUltrasound is an ultrasound dataset collected at Duke University with a Verasonics c52v probe. It contains delay-and-sum (DAS) beamformed data as well as data post-processed with Siemens Dynamic TCE for speckle reduction, contrast enhancement and improvement in conspicuity of anatomical structures. These data were collected with support from the National Institute of Biomedical Imaging and Bioengineering under Grant R01-EB026574 and National Institutes of Health under Grant 5T32GM007171-44. A usage example is available here.

Split Examples
'A' 1,362
'B' 1,194
'MARK' 420
'test' 438
'train' 2,556
'validation' 278
  • Feature structure:
FeaturesDict({
    'das': FeaturesDict({
        'dB': Tensor(shape=(None,), dtype=float32),
        'imag': Tensor(shape=(None,), dtype=float32),
        'real': Tensor(shape=(None,), dtype=float32),
    }),
    'dtce': Tensor(shape=(None,), dtype=float32),
    'f0_hz': float32,
    'final_angle': float32,
    'final_radius': float32,
    'focus_cm': float32,
    'harmonic': bool,
    'height': uint32,
    'initial_angle': float32,
    'initial_radius': float32,
    'probe': object,
    'scanner': object,
    'target': object,
    'timestamp_id': uint32,
    'voltage': float32,
    'width': uint32,
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
das FeaturesDict
das/dB Tensor (None,) float32
das/imag Tensor (None,) float32
das/real Tensor (None,) float32
dtce Tensor (None,) float32
f0_hz Tensor float32
final_angle Tensor float32
final_radius Tensor float32
focus_cm Tensor float32
harmonic Tensor bool
height Tensor uint32
initial_angle Tensor float32
initial_radius Tensor float32
probe Tensor object
scanner Tensor object
target Tensor object
timestamp_id Tensor uint32
voltage Tensor float32
width Tensor uint32
  • Citation:
@article{DBLP:journals/corr/abs-1908-05782,
  author    = {Ouwen Huang and
               Will Long and
               Nick Bottenus and
               Gregg E. Trahey and
               Sina Farsiu and
               Mark L. Palmeri},
  title     = {MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box
               Constraints},
  journal   = {CoRR},
  volume    = {abs/1908.05782},
  year      = {2019},
  url       = {http://arxiv.org/abs/1908.05782},
  archivePrefix = {arXiv},
  eprint    = {1908.05782},
  timestamp = {Mon, 19 Aug 2019 13:21:03 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1908-05782},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}