Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep learning based 2.5D flow field estimation for maximum intensity
  projections of 4D optical coherence tomography   In microsurgery, lasers have emerged as precise tools for bone ablation. A
challenge is automatic control of laser bone ablation with 4D optical coherence
tomography (OCT). OCT as high resolution imaging modality provides volumetric
images of tissue and foresees information of bone position and orientation
(pose) as well as thickness. However, existing approaches for OCT based laser
ablation control rely on external tracking systems or invasively ablated
artificial landmarks for tracking the pose of the OCT probe relative to the
tissue. This can be superseded by estimating the scene flow caused by relative
movement between OCT-based laser ablation system and patient.
  Therefore, this paper deals with 2.5D scene flow estimation of volumetric OCT
images for application in laser ablation. We present a semi-supervised
convolutional neural network based tracking scheme for subsequent 3D OCT
volumes and apply it to a realistic semi-synthetic data set of ex vivo human
temporal bone specimen. The scene flow is estimated in a two-stage approach. In
the first stage, 2D lateral scene flow is computed on census-transformed
en-face arguments-of-maximum intensity projections. Subsequent to this, the
projections are warped by predicted lateral flow and 1D depth flow is
estimated. The neural network is trained semi-supervised by combining error to
ground truth and the reconstruction error of warped images with assumptions of
spatial flow smoothness. Quantitative evaluation reveals a mean endpoint error
of $ (4.7\pm{}3.5) $ voxel or $ 27.5 \pm 20.5 \mu\mathrm{m} $ for scene flow
estimation caused by simulated relative movement between the OCT probe and
bone. The scene flow estimation for 4D OCT enables its use for markerless
tracking of mastoid bone structures for image guidance in general, and
automated laser ablation control.

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The categories of this paper are: physics
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: q-fin
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The categories of this paper are: cond-mat
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The categories of this paper are: eess
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The categories of this paper are: hep-ex
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The categories of this paper are: ao-sci
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The categories of this paper are: nlin
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The categories of this paper are: math
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The categories of this paper are: funct-an
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The categories of this paper are: q-alg
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The categories of this paper are: astro-ph
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The categories of this paper are: math-ph
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The categories of this paper are: econ