  Due to a high heterogeneity in pose and size and to a limited number of
available data, segmentation of pediatric images is challenging for deep
learning methods. In this work, we propose a new CNN architecture that is pose
and scale invariant thanks to the use of Spatial Transformer Network (STN). Our
architecture is composed of three sequential modules that are estimated
together during training: (i) a regression module to estimate a similarity
matrix to normalize the input image to a reference one; (ii) a differentiable
module to find the region of interest to segment; (iii) a segmentation module,
based on the popular UNet architecture, to delineate the object. Unlike the
original UNet, which strives to learn a complex mapping, including pose and
scale variations, from a finite training dataset, our segmentation module
learns a simpler mapping focusing on images with normalized pose and size.
Furthermore, the use of an automatic bounding box detection through STN allows
saving time and especially memory, while keeping similar performance. We test
the proposed method in kidney and renal tumor segmentation on abdominal
pediatric CT scanners. Results indicate that the estimated STN homogenization
of size and pose accelerates the segmentation (25h), compared to standard
data-augmentation (33h), while obtaining a similar quality for the kidney
(88.01\% of Dice score) and improving the renal tumor delineation (from 85.52\%
to 87.12\%).
