Recent advances in deep learning, like 3D fully convolutional networks
(FCNs), have improved the state-of-the-art in dense semantic segmentation of
medical images. However, most network architectures require severely
downsampling or cropping the images to meet the memory limitations of today's
GPU cards while still considering enough context in the images for accurate
segmentation. In this work, we propose a novel approach that utilizes
auto-context to perform semantic segmentation at higher resolutions in a
multi-scale pyramid of stacked 3D FCNs. We train and validate our models on a
dataset of manually annotated abdominal organs and vessels from 377 clinical CT
images used in gastric surgery, and achieve promising results with close to 90%
Dice score on average. For additional evaluation, we perform separate testing
on datasets from different sources and achieve competitive results,
illustrating the robustness of the model and approach.

本文提出一种新的方法通过采用自动上下文来利用多尺度堆叠 3D FCN 金字塔，从而提高分辨率执行语义分割。 我们在手动注释的 377 个胃手术的 CT 影像数据集上训练和验证模型，并获得近 90％的 Dice 分数。

用于腹部多器官分割的多尺度金字塔 3D 全卷积网络

A multi-scale pyramid of 3D fully convolutional networks for abdominal  multi-organ segmentation

We present a novel deep learning framework that models the scene dependent
image processing inside cameras. Often called as the radiometric calibration,
the process of recovering RAW images from processed images (JPEG format in the
sRGB color space) is essential for many computer vision tasks that rely on
physically accurate radiance values. All previous works rely on the
deterministic imaging model where the color transformation stays the same
regardless of the scene and thus they can only be applied for images taken
under the manual mode. In this paper, we propose a data-driven approach to
learn the scene dependent and locally varying image processing inside cameras
under the automode. Our method incorporates both the global and the local scene
context into pixel-wise features via multi-scale pyramid of learnable histogram
layers. The results show that we can model the imaging pipeline of different
cameras that operate under the automode accurately in both directions (from RAW
to sRGB, from sRGB to RAW) and we show how we can apply our method to improve
the performance of image deblurring.

本文提出了一种新颖的深度学习框架，用于对场景相关的相机内图像处理进行建模，通过多尺度金字塔学习直方图层将全局和局部场景上下文结合到像素特征中，该方法可以精确地建模在自动模式下不同相机的成像管道，并应用于改善图像去模糊的性能。