Grotesque body images

Kate Mondloch, A Capsule Aesthetic: Feminist Materialisms in New Media Art (Minneapolis: University of Minnesota Press, 2018), 151 pp. ISBN: 9781517900496 (paperback, $27) Alberto Brodesco and Federico Giordano, editors, Body Images in the Post-Cinematic Scenario: The Digitization of Bodies (Milan: Mimesis International, 2017). 195 pp., ISBN: 9788869771095 (paperback, $27.50) Cynthia J. Miller and A. Bowdoin Van Riper, editors, What’s Eating You? Food and Horror on Screen (New York: Bloomsbury Academic, 2017). 370pp., ISBN: 9781501322389 (hardback, $105); ISBN: 9781501343964 (paperback, $27.96); ISBN: 9781501322419 (ebook, $19.77) Kaya Davies Hayon, Sensuous Cinema: The Body in Contemporary Maghrebi Cinema (New York: Bloomsbury, 2018). 181pp., ISBN: 9781501335983 (hardback, $107.99)

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From an Archaeology of Iconoclasm to an Anthropology of the Body: Images, Punishment, and Personhood in England, 1500-1660 [with Comments]

Current Anthropology ◽

10.2307/20142603 ◽

2008 ◽

Vol 49 (1) ◽

pp. 35

Author(s):

Graves

Keyword(s):

The Body ◽

Body Images ◽

Anthropology Of The Body

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A Multi-Channel Uncertainty-Aware Multi-Resolution Network for MR to CT Synthesis

Applied Sciences ◽

10.3390/app11041667 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1667

Author(s):

Kerstin Klaser ◽

Pedro Borges ◽

Richard Shaw ◽

Marta Ranzini ◽

Marc Modat ◽

...

Keyword(s):

Medical Image Analysis ◽

Absolute Error ◽

Magnetic Resonance Images ◽

The Body ◽

Whole Body ◽

Image Size ◽

Body Images ◽

Image Translation ◽

The Mean ◽

Synthetic Ct

Synthesising computed tomography (CT) images from magnetic resonance images (MRI) plays an important role in the field of medical image analysis, both for quantification and diagnostic purposes. Convolutional neural networks (CNNs) have achieved state-of-the-art results in image-to-image translation for brain applications. However, synthesising whole-body images remains largely uncharted territory, involving many challenges, including large image size and limited field of view, complex spatial context, and anatomical differences between images acquired at different times. We propose the use of an uncertainty-aware multi-channel multi-resolution 3D cascade network specifically aiming for whole-body MR to CT synthesis. The Mean Absolute Error on the synthetic CT generated with the MultiResunc network (73.90 HU) is compared to multiple baseline CNNs like 3D U-Net (92.89 HU), HighRes3DNet (89.05 HU) and deep boosted regression (77.58 HU) and shows superior synthesis performance. We ultimately exploit the extrapolation properties of the MultiRes networks on sub-regions of the body.

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