【论文推荐】最新七篇图像分割相关论文—半监督学习、多源域适应、多器官分割、知识全卷积网络、Quickshift++

2018 年 6 月 3 日 专知

【导读】专知内容组在前两天推出十四篇图像分割（Image Segmentation）相关文章，又推出近期七篇图像分割相关文章，为大家进行介绍，欢迎查看!

15.Complex Relations in a Deep Structured Prediction Model for Fine Image Segmentation（精细图像分割的深层结构预测模型中的复杂关系）

作者：Cristina Mata,Guy Ben-Yosef,Boris Katz

机构：MIT

摘要：Many deep learning architectures for semantic segmentation involve a Fully Convolutional Neural Network (FCN) followed by a Conditional Random Field (CRF) to carry out inference over an image. These models typically involve unary potentials based on local appearance features computed by FCNs, and binary potentials based on the displacement between pixels. We show that while current methods succeed in segmenting whole objects, they perform poorly in situations involving a large number of object parts. We therefore suggest incorporating into the inference algorithm additional higher-order potentials inspired by the way humans identify and localize parts. We incorporate two relations that were shown to be useful to human object identification - containment and attachment - into the energy term of the CRF and evaluate their performance on the Pascal VOC Parts dataset. Our experimental results show that the segmentation of fine parts is positively affected by the addition of these two relations, and that the segmentation of fine parts can be further influenced by complex structural features.

期刊：arXiv, 2018年5月24日

网址：

http://www.zhuanzhi.ai/document/7d2545cbdc7a04278cc52d3de82de9b4

16.Consensus Based Medical Image Segmentation Using Semi-Supervised Learning And Graph Cuts（使用半监督学习和图切割实现基于共识的医学图像分割）

作者：Dwarikanath Mahapatra

摘要：Medical image segmentation requires consensus ground truth segmentations to be derived from multiple expert annotations. A novel approach is proposed that obtains consensus segmentations from experts using graph cuts (GC) and semi supervised learning (SSL). Popular approaches use iterative Expectation Maximization (EM) to estimate the final annotation and quantify annotator's performance. Such techniques pose the risk of getting trapped in local minima. We propose a self consistency (SC) score to quantify annotator consistency using low level image features. SSL is used to predict missing annotations by considering global features and local image consistency. The SC score also serves as the penalty cost in a second order Markov random field (MRF) cost function optimized using graph cuts to derive the final consensus label. Graph cut obtains a global maximum without an iterative procedure. Experimental results on synthetic images, real data of Crohn's disease patients and retinal images show our final segmentation to be accurate and more consistent than competing methods.

期刊：arXiv, 2018年5月21日

网址：

http://www.zhuanzhi.ai/document/76ffe65a6e67d6881a80aaca309cce40

17.Optimal Transport for Multi-source Domain Adaptation under Target Shift（目标转移下多源域适应的最优传输）

作者：Ievgen Redko,Nicolas Courty,Rémi Flamary,Devis Tuia

机构：University of Côte d’Azur

摘要：In this paper, we propose to tackle the problem of reducing discrepancies between multiple domains referred to as multi-source domain adaptation and consider it under the target shift assumption: in all domains we aim to solve a classification problem with the same output classes, but with labels' proportions differing across them. This problem, generally ignored in the vast majority papers on domain adaptation papers, is nevertheless critical in real-world applications, and we theoretically show its impact on the adaptation success. To address this issue, we design a method based on optimal transport, a theory that has been successfully used to tackle adaptation problems in machine learning. Our method performs multi-source adaptation and target shift correction simultaneously by learning the class probabilities of the unlabeled target sample and the coupling allowing to align two (or more) probability distributions. Experiments on both synthetic and real-world data related to satellite image segmentation task show the superiority of the proposed method over the state-of-the-art.

期刊：arXiv, 2018年5月23日

网址：

http://www.zhuanzhi.ai/document/f595a99d5126f65ed79ec992fd746d2e

18.Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation（Combo Loss:处理输入和输出不平衡的多器官分割）

作者：Saeid Asgari Taghanaki,Yefeng Zheng,S. Kevin Zhou,Bogdan Georgescu,Puneet Sharma,Daguang Xu,Dorin Comaniciu,Ghassan Hamarneh

机构：Simon Fraser University

摘要：Simultaneous segmentation of multiple organs from different medical imaging modalities is a crucial task as it can be utilized for computer-aided diagnosis, computer-assisted surgery, and therapy planning. Thanks to the recent advances in deep learning, several deep neural networks for medical image segmentation have been introduced successfully for this purpose. In this paper, we focus on learning a deep multi-organ segmentation network that labels voxels. In particular, we examine the critical choice of a loss function in order to handle the notorious imbalance problem that plagues both the input and output of a learning model. The input imbalance refers to the class-imbalance in the input training samples (i.e. small foreground objects embedded in an abundance of background voxels, as well as organs of varying sizes). The output imbalance refers to the imbalance between the false positives and false negatives of the inference model. We introduce a loss function that integrates a weighted cross-entropy with a Dice similarity coefficient to tackle both types of imbalance during training and inference. We evaluated the proposed loss function on three datasets of whole body PET scans with 5 target organs, MRI prostate scans, and ultrasound echocardigraphy images with a single target organ. We show that a simple network architecture with the proposed integrative loss function can outperform state-of-the-art methods and results of the competing methods can be improved when our proposed loss is used.

期刊：arXiv, 2018年5月23日

网址：

http://www.zhuanzhi.ai/document/317656929dd48745fcab1ff6e7df216b

19.Convexity Shape Prior for Level Set based Image Segmentation Method（基于凸度形状先验的层次集的图像分割方法）

作者：Shi Yan,Xue-cheng Tai,Jun Liu,Hai-yang Huang

摘要：We propose a geometric convexity shape prior preservation method for variational level set based image segmentation methods. Our method is built upon the fact that the level set of a convex signed distanced function must be convex. This property enables us to transfer a complicated geometrical convexity prior into a simple inequality constraint on the function. An active set based Gauss-Seidel iteration is used to handle this constrained minimization problem to get an efficient algorithm. We apply our method to region and edge based level set segmentation models including Chan-Vese (CV) model with guarantee that the segmented region will be convex. Experimental results show the effectiveness and quality of the proposed model and algorithm.

期刊：arXiv, 2018年5月22日

网址：

http://www.zhuanzhi.ai/document/eb8e91fa235c223ef86b1312e737d294

20.Knowledge-based Fully Convolutional Network and Its Application in Segmentation of Lung CT Images（基于知识的全卷积网络及其在肺CT图像分割中的应用）

作者：Tao Yu,Yu Qiao,Huan Long

机构：Shanghai Jiao Tong University

摘要：A variety of deep neural networks have been applied in medical image segmentation and achieve good performance. Unlike natural images, medical images of the same imaging modality are characterized by the same pattern, which indicates that same normal organs or tissues locate at similar positions in the images. Thus, in this paper we try to incorporate the prior knowledge of medical images into the structure of neural networks such that the prior knowledge can be utilized for accurate segmentation. Based on this idea, we propose a novel deep network called knowledge-based fully convolutional network (KFCN) for medical image segmentation. The segmentation function and corresponding error is analyzed. We show the existence of an asymptotically stable region for KFCN which traditional FCN doesn't possess. Experiments validate our knowledge assumption about the incorporation of prior knowledge into the convolution kernels of KFCN and show that KFCN can achieve a reasonable segmentation and a satisfactory accuracy.

期刊：arXiv, 2018年5月22日

网址：

http://www.zhuanzhi.ai/document/9ad295af7a4e3a5f3fcfc8ea8147e5d7

21.Quickshift++: Provably Good Initializations for Sample-Based Mean Shift（Quickshift++: 对于基于样本的平均偏移进行很好的初始化）

作者：Heinrich Jiang,Jennifer Jang,Samory Kpotufe

ICML 2018. Code release: https://github.com/google/quickshift

摘要：We provide initial seedings to the Quick Shift clustering algorithm, which approximate the locally high-density regions of the data. Such seedings act as more stable and expressive cluster-cores than the singleton modes found by Quick Shift. We establish statistical consistency guarantees for this modification. We then show strong clustering performance on real datasets as well as promising applications to image segmentation.