October 2020
Volume 20, Issue 11
Open Access
Vision Sciences Society Annual Meeting Abstract  |   October 2020
Searching high and low for the gist in 3D medical images
Author Affiliations
  • Melissa Trevino
    National Cancer Institute
  • Baris Turkbey
    National Cancer Institute
  • Mark Lowry
    National Cancer Institute
  • Bradford Wood
    National Cancer Institute
  • Peter Pinto
    National Cancer Institute
  • Marcin Czarniecki
    Georgetown University School of Medicine
  • Peter Choyke
    National Cancer Institute
  • Todd Horowitz
    National Cancer Institute
Journal of Vision October 2020, Vol.20, 1573. doi:https://doi.org/10.1167/jov.20.11.1573
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      Melissa Trevino, Baris Turkbey, Mark Lowry, Bradford Wood, Peter Pinto, Marcin Czarniecki, Peter Choyke, Todd Horowitz; Searching high and low for the gist in 3D medical images. Journal of Vision 2020;20(11):1573. https://doi.org/10.1167/jov.20.11.1573.

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      © ARVO (1962-2015); The Authors (2016-present)

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Radiologists can identify the gist of a medical image (abnormal vs. normal) better than chance in static 2D images, after presentations of half a second or less (Evans et al., 2013; 2016). The gist of 2D real-world scenes is carried by low spatial frequency channels, which convey the structural layout of scenes (Schyns & Oliva, 1994). In contrast the gist signal in 2D mammography is carried by high spatial frequency channels (Evans et al. 2016). Standard practice in radiology is moving to 3D modalities, where each case consists of a series of images that are assembled into a virtual stack. Radiologists can extract gist from movies of these stacks (Trevino et al., 2019; Wu & Wolfe, 2019). We do not know which channels carry the gist from 3D stacks. We tested 51 radiologists with prostate mpMRI experience on 56 cases, each comprising a stack of 26 T2-weighted prostate mpMRI images. Lesions (Gleason scores 6-9) were present in 50% of cases. A trial consisted of a movie of a single case presented at 48 ms/slice. After each case, participants localized the cancerous lesion on a prostate sector map, then indicated whether a cancerous lesion was presented, and gave a confidence rating. Radiologists were divided equally into three groups who viewed either unfiltered images, low-pass (< 2 cycles/°) filtered images, or high-pass (> 6 cycles/°) filtered images. Unfiltered detection and localization performance were higher than chance (d’ = 0.28; localization = 31%). Radiologists performed at chance when detecting lesions in high-pass filtered images (d’ = 0.16), and were significantly lower than chance for low-pass filtered images (d’ = -0.23). Our data indicate that gist perception from 3D prostate MRI relies on spatial frequency channels between 2 and 6 cycles/°. These findings emphasize that scene gist is highly dependent on task and context.


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