Brain MR image segmentation is a key task in neuroimaging studies. It is
commonly conducted using standard computational tools, such as FSL, SPM,
multi-atlas segmentation etc, which are often registration-based and suffer
from expensive computation cost. Recently, there is an increased interest using
deep neural networks for brain image segmentation, which have demonstrated
advantages in both speed and performance. However, neural networks-based
approaches normally require a large amount of manual annotations for optimising
the massive amount of network parameters. For 3D networks used in volumetric
image segmentation, this has become a particular challenge, as a 3D network
consists of many more parameters compared to its 2D counterpart. Manual
annotation of 3D brain images is extremely time-consuming and requires
extensive involvement of trained experts. To address the challenge with limited
manual annotations, here we propose a novel multi-task learning framework for
brain image segmentation, which utilises a large amount of automatically
generated partial annotations together with a small set of manually created
full annotations for network training. Our method yields a high performance
comparable to state-of-the-art methods for whole brain segmentation.

本研究提出了一种新的多任务学习框架，利用大量自动生成的部分注释和少量手工创建的完整注释进行网络训练，解决了使用深度神经网络进行 3D 脑图像分割的挑战，同时提高了全脑分割的性能。

使用部分注释进行全脑分割的迁移学习

Transfer Learning from Partial Annotations for Whole Brain Segmentation

Plant root research can provide a way to attain stress-tolerant crops that
produce greater yield in a diverse array of conditions. Phenotyping roots in
soil is often challenging due to the roots being difficult to access and the
use of time consuming manual methods. Rhizotrons allow visual inspection of
root growth through transparent surfaces. Agronomists currently manually label
photographs of roots obtained from rhizotrons using a line-intersect method to
obtain root length density and rooting depth measurements which are essential
for their experiments. We investigate the effectiveness of an automated image
segmentation method based on the U-Net Convolutional Neural Network (CNN)
architecture to enable such measurements. We design a data-set of 50 annotated
Chicory (Cichorium intybus L.) root images which we use to train, validate and
test the system and compare against a baseline built using the Frangi
vesselness filter. We obtain metrics using manual annotations and
line-intersect counts. Our results on the held out data show our proposed
automated segmentation system to be a viable solution for detecting and
quantifying roots. We evaluate our system using 867 images for which we have
obtained line-intersect counts, attaining a Spearman rank correlation of 0.9748
and an $r^2$ of 0.9217. We also achieve an $F_1$ of 0.7 when comparing the
automated segmentation to the manual annotations, with our automated
segmentation system producing segmentations with higher quality than the manual
annotations for large portions of the image.

使用 U-Net 卷积神经网络对植物根系图像进行自动分割，实现了根长度密度和深度测量的自动测量，结果表明自动分割比人工注释更准确。

使用 U-Net 对土壤中的根进行分割

Segmentation of Roots in Soil with U-Net

Recent advances with Convolutional Networks (ConvNets) have shifted the
bottleneck for many computer vision tasks to annotated data collection. In this
paper, we present a geometry-driven approach to automatically collect
annotations for human pose prediction tasks. Starting from a generic ConvNet
for 2D human pose, and assuming a multi-view setup, we describe an automatic
way to collect accurate 3D human pose annotations. We capitalize on constraints
offered by the 3D geometry of the camera setup and the 3D structure of the
human body to probabilistically combine per view 2D ConvNet predictions into a
globally optimal 3D pose. This 3D pose is used as the basis for harvesting
annotations. The benefit of the annotations produced automatically with our
approach is demonstrated in two challenging settings: (i) fine-tuning a generic
ConvNet-based 2D pose predictor to capture the discriminative aspects of a
subject's appearance (i.e.,"personalization"), and (ii) training a ConvNet from
scratch for single view 3D human pose prediction without leveraging 3D pose
groundtruth. The proposed multi-view pose estimator achieves state-of-the-art
results on standard benchmarks, demonstrating the effectiveness of our method
in exploiting the available multi-view information.

本文提出了一种基于几何驱动的方法来自动收集人体姿势预测任务的准确的三维人体姿势标注，利用多视角设置、相机设置的 3D 几何和人体身体结构的约束，将每个视图的 2D ConvNet 预测概率组合成全局最优 3D 姿态。