  Deep learning methods have surpassed the performance of traditional
techniques on a wide range of problems in computer vision, but nearly all of
this work has studied consumer photos, where precisely correct output is often
not critical. It is less clear how well these techniques may apply on
structured prediction problems where fine-grained output with high precision is
required, such as in scientific imaging domains. Here we consider the problem
of segmenting echogram radar data collected from the polar ice sheets, which is
challenging because segmentation boundaries are often very weak and there is a
high degree of noise. We propose a multi-task spatiotemporal neural network
that combines 3D ConvNets and Recurrent Neural Networks (RNNs) to estimate ice
surface boundaries from sequences of tomographic radar images. We show that our
model outperforms the state-of-the-art on this problem by (1) avoiding the need
for hand-tuned parameters, (2) extracting multiple surfaces (ice-air and
ice-bed) simultaneously, (3) requiring less non-visual metadata, and (4) being
about 6 times faster.
