In this paper, we study the influence of both long and short skip connections
on Fully Convolutional Networks (FCN) for biomedical image segmentation. In
standard FCNs, only long skip connections are used to skip features from the
contracting path to the expanding path in order to recover spatial information
lost during downsampling. We extend FCNs by adding short skip connections, that
are similar to the ones introduced in residual networks, in order to build very
deep FCNs (of hundreds of layers). A review of the gradient flow confirms that
for a very deep FCN it is beneficial to have both long and short skip
connections. Finally, we show that a very deep FCN can achieve
near-to-state-of-the-art results on the EM dataset without any further
post-processing.

本研究探讨长短跳跃连接对全卷积网络进行生物医学图像分割的影响，发现在非常深的 FCN 中同时使用长、短跳跃连接是有益的，并展示了非常深的 FCN 在 EM 数据集上实现了接近最新成果的结果而无需进一步的后处理。

生物医学图像分割中跳跃连接的重要性

The Importance of Skip Connections in Biomedical Image Segmentation

Multi-atlas segmentation (MAS), first introduced and popularized by the
pioneering work of Rohlfing, Brandt, Menzel and Maurer Jr (2004), Klein, Mensh,
Ghosh, Tourville and Hirsch (2005), and Heckemann, Hajnal, Aljabar, Rueckert
and Hammers (2006), is becoming one of the most widely-used and successful
image segmentation techniques in biomedical applications. By manipulating and
utilizing the entire dataset of "atlases" (training images that have been
previously labeled, e.g., manually by an expert), rather than some model-based
average representation, MAS has the flexibility to better capture anatomical
variation, thus offering superior segmentation accuracy. This benefit, however,
typically comes at a high computational cost. Recent advancements in computer
hardware and image processing software have been instrumental in addressing
this challenge and facilitated the wide adoption of MAS. Today, MAS has come a
long way and the approach includes a wide array of sophisticated algorithms
that employ ideas from machine learning, probabilistic modeling, optimization,
and computer vision, among other fields. This paper presents a survey of
published MAS algorithms and studies that have applied these methods to various
biomedical problems. In writing this survey, we have three distinct aims. Our
primary goal is to document how MAS was originally conceived, later evolved,
and now relates to alternative methods. Second, this paper is intended to be a
detailed reference of past research activity in MAS, which now spans over a
decade (2003 - 2014) and entails novel methodological developments and
application-specific solutions. Finally, our goal is to also present a
perspective on the future of MAS, which, we believe, will be one of the
dominant approaches in biomedical image segmentation.

本文介绍了多个已发表的 MAS 算法和应用这些方法于各种生物医学问题的研究，并将 MAS 与其他方法进行比较和总结， 讨论了 MAS 的过去、现在和未来的发展趋势。