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duke_ultrasound

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 avalible here.

Features

FeaturesDict({
    'das': FeaturesDict({
        'dB': Tensor(shape=(None,), dtype=tf.float32),
        'imag': Tensor(shape=(None,), dtype=tf.float32),
        'real': Tensor(shape=(None,), dtype=tf.float32),
    }),
    'dtce': Tensor(shape=(None,), dtype=tf.float32),
    'f0_hz': Tensor(shape=(), dtype=tf.float32),
    'final_angle': Tensor(shape=(), dtype=tf.float32),
    'final_radius': Tensor(shape=(), dtype=tf.float32),
    'focus_cm': Tensor(shape=(), dtype=tf.float32),
    'harmonic': Tensor(shape=(), dtype=tf.bool),
    'height': Tensor(shape=(), dtype=tf.uint32),
    'initial_angle': Tensor(shape=(), dtype=tf.float32),
    'initial_radius': Tensor(shape=(), dtype=tf.float32),
    'probe': Tensor(shape=(), dtype=tf.string),
    'scanner': Tensor(shape=(), dtype=tf.string),
    'target': Tensor(shape=(), dtype=tf.string),
    'timestamp_id': Tensor(shape=(), dtype=tf.uint32),
    'voltage': Tensor(shape=(), dtype=tf.float32),
    'width': Tensor(shape=(), dtype=tf.uint32),
})

Statistics

Split Examples
ALL 6,248
TRAIN 2,556
A 1,362
B 1,194
TEST 438
MARK 420
VALIDATION 278

Homepage

Supervised keys (for as_supervised=True)

(u'das/dB', u'dtce')

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}
}