January, 2021

A representative subject was evaluated with URN (left) and BUSN (right). Green scatters are inliers of influence to the regression, yellow
scatters are outliers of no influence to the distributed data. Darker blue line indicates the normal linear regression on scatters points, lighter blue
line is the RANSAC regressor result according to inliers. Left panel presents the single URN regression with amounts of outliers result in failure of
linearity nature in chest and pelvis regions. Right panel shows the testing result of BUSN method, the distributed scores follows good linearity in
chest, abdomen and pelvis regions in CT scan. In summary, BUSN takes advantage of self-supervised network, which presents better continuity in
regression result among neighbor slices and shows scatter plots without number of outliers.

Body Part Regression With Self-Supervision

Jan. 14, 2021—Y.Tang, R.Gao, S.Han, Y.Chen, D.Gao, V.Nath, C.Bermudez, M.R. Savona, R.G. Abramson, S.Bao,I.Lyu, Y.Huo and B.A. Landman,”Body Part Regression with Self-supervision”,IEEETransactions onMedicalImaging,2021 Full Text: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9350603 Abstract Body part regression is a promising new technique that enables content navigation through selfsupervised learning. Using this technique, the global quantitative spatial location for each axial view slice is obtained...