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图像分割就是把图像分成若干个特定的、具有独特性质的区域并提出感兴趣目标的技术和过程。它是由图像处理到图像分析的关键步骤。 所谓图像分割指的是根据灰度、颜色、纹理和形状等特征把图像划分成若干互不交迭的区域，并使这些特征在同一区域内呈现出相似性，而在不同区域间呈现出明显的差异性。

知识荟萃

图像分割 （Image Segmentation) 专知荟萃

入门学习

  1. A 2017 Guide to Semantic Segmentation with Deep Learning 概述——用深度学习做语义分割
  2. 从全卷积网络到大型卷积核：深度学习的语义分割全指南
  3. Fully Convolutional Networks
  4. 语义分割中的深度学习方法全解：从FCN、SegNet到各代DeepLab
  5. 图像语义分割之FCN和CRF
  6. 从特斯拉到计算机视觉之「图像语义分割」
  7. 计算机视觉之语义分割
  8. Segmentation Results: VOC2012 PASCAL语义分割比赛排名

综述

  1. A Review on Deep Learning Techniques Applied to Semantic Segmentation Alberto Garcia-Garcia, Sergio Orts-Escolano, Sergiu Oprea, Victor Villena-Martinez, Jose Garcia-Rodriguez 2017
  2. Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art
  3. 基于内容的图像分割方法综述 姜 枫 顾 庆 郝慧珍 李 娜 郭延文 陈道蓄 2017

进阶论文

  1. U-Net [https://arxiv.org/pdf/1505.04597.pdf]
  2. SegNet [https://arxiv.org/pdf/1511.00561.pdf]
  3. DeepLab [https://arxiv.org/pdf/1606.00915.pdf]
  4. FCN [https://arxiv.org/pdf/1605.06211.pdf]
  5. ENet [https://arxiv.org/pdf/1606.02147.pdf]
  6. LinkNet [https://arxiv.org/pdf/1707.03718.pdf]
  7. DenseNet [https://arxiv.org/pdf/1608.06993.pdf]
  8. Tiramisu [https://arxiv.org/pdf/1611.09326.pdf]
  9. DilatedNet [https://arxiv.org/pdf/1511.07122.pdf]
  10. PixelNet [https://arxiv.org/pdf/1609.06694.pdf]
  11. ICNet [https://arxiv.org/pdf/1704.08545.pdf]
  12. ERFNet [http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17iv.pdf]
  13. RefineNet [https://arxiv.org/pdf/1611.06612.pdf]
  14. PSPNet [https://arxiv.org/pdf/1612.01105.pdf]
  15. CRFasRNN [http://www.robots.ox.ac.uk/%7Eszheng/papers/CRFasRNN.pdf]
  16. Dilated convolution [https://arxiv.org/pdf/1511.07122.pdf]
  17. DeconvNet [https://arxiv.org/pdf/1505.04366.pdf]
  18. FRRN [https://arxiv.org/pdf/1611.08323.pdf]
  19. GCN [https://arxiv.org/pdf/1703.02719.pdf]
  20. DUC, HDC [https://arxiv.org/pdf/1702.08502.pdf]
  21. Segaware [https://arxiv.org/pdf/1708.04607.pdf]
  22. Semantic Segmentation using Adversarial Networks [https://arxiv.org/pdf/1611.08408.pdf]

综述

  1. A Review on Deep Learning Techniques Applied to Semantic Segmentation Alberto Garcia-Garcia, Sergio Orts-Escolano, Sergiu Oprea, Victor Villena-Martinez, Jose Garcia-Rodriguez 2017
  2. Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art
  3. 基于内容的图像分割方法综述 姜 枫 顾 庆 郝慧珍 李 娜 郭延文 陈道蓄 2017

Tutorial

  1. Semantic Image Segmentation with Deep Learning
  2. A 2017 Guide to Semantic Segmentation with Deep Learning
  3. Image Segmentation with Tensorflow using CNNs and Conditional Random Fields

视频教程

  1. CS231n: Convolutional Neural Networks for Visual Recognition Lecture 11 Detection and Segmentation 
  2. Machine Learning for Semantic Segmentation - Basics of Modern Image Analysis

代码

Semantic segmentation

  1. U-Net (https://arxiv.org/pdf/1505.04597.pdf)
  2. SegNet (https://arxiv.org/pdf/1511.00561.pdf)
  3. DeepLab (https://arxiv.org/pdf/1606.00915.pdf)
  4. FCN (https://arxiv.org/pdf/1605.06211.pdf)
  5. ENet (https://arxiv.org/pdf/1606.02147.pdf)
  6. LinkNet (https://arxiv.org/pdf/1707.03718.pdf)
  7. DenseNet (https://arxiv.org/pdf/1608.06993.pdf)
  8. Tiramisu (https://arxiv.org/pdf/1611.09326.pdf)
  9. DilatedNet (https://arxiv.org/pdf/1511.07122.pdf)
  10. PixelNet (https://arxiv.org/pdf/1609.06694.pdf)
  11. ICNet (https://arxiv.org/pdf/1704.08545.pdf)
  12. ERFNet (http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17iv.pdf)
  13. RefineNet (https://arxiv.org/pdf/1611.06612.pdf)
  14. PSPNet (https://arxiv.org/pdf/1612.01105.pdf)
  15. CRFasRNN (http://www.robots.ox.ac.uk/%7Eszheng/papers/CRFasRNN.pdf)
  16. Dilated convolution (https://arxiv.org/pdf/1511.07122.pdf)
  17. DeconvNet (https://arxiv.org/pdf/1505.04366.pdf)
  18. FRRN (https://arxiv.org/pdf/1611.08323.pdf)
  19. GCN (https://arxiv.org/pdf/1703.02719.pdf)
  20. DUC, HDC (https://arxiv.org/pdf/1702.08502.pdf)
  21. Segaware (https://arxiv.org/pdf/1708.04607.pdf)
  22. Semantic Segmentation using Adversarial Networks (https://arxiv.org/pdf/1611.08408.pdf)

Instance aware segmentation

  1. FCIS [https://arxiv.org/pdf/1611.07709.pdf]
  2. MNC [https://arxiv.org/pdf/1512.04412.pdf]
  3. DeepMask [https://arxiv.org/pdf/1506.06204.pdf]
  4. SharpMask [https://arxiv.org/pdf/1603.08695.pdf]
  5. Mask-RCNN [https://arxiv.org/pdf/1703.06870.pdf]
  6. https://github.com/jasjeetIM/Mask-RCNN [Caffe]
  7. RIS [https://arxiv.org/pdf/1511.08250.pdf]
  8. FastMask [https://arxiv.org/pdf/1612.08843.pdf]

Satellite images segmentation

Video segmentation

Autonomous driving

Annotation Tools:

Datasets

  1. Stanford Background Dataset[http://dags.stanford.edu/projects/scenedataset.html]
  2. Sift Flow Dataset[http://people.csail.mit.edu/celiu/SIFTflow/]
  3. Barcelona Dataset[http://www.cs.unc.edu/~jtighe/Papers/ECCV10/]
  4. Microsoft COCO dataset[http://mscoco.org/]
  5. MSRC Dataset[http://research.microsoft.com/en-us/projects/objectclassrecognition/]
  6. LITS Liver Tumor Segmentation Dataset[https://competitions.codalab.org/competitions/15595]
  7. KITTI[http://www.cvlibs.net/datasets/kitti/eval_road.php]
  8. Stanford background dataset[http://dags.stanford.edu/projects/scenedataset.html]
  9. Data from Games dataset[https://download.visinf.tu-darmstadt.de/data/from_games/]
  10. Human parsing dataset[https://github.com/lemondan/HumanParsing-Dataset]
  11. Silenko person database[https://github.com/Maxfashko/CamVid]
  12. Mapillary Vistas Dataset[https://www.mapillary.com/dataset/vistas]
  13. Microsoft AirSim[https://github.com/Microsoft/AirSim]
  14. MIT Scene Parsing Benchmark[http://sceneparsing.csail.mit.edu/]
  15. COCO 2017 Stuff Segmentation Challenge[http://cocodataset.org/#stuff-challenge2017]
  16. ADE20K Dataset[http://groups.csail.mit.edu/vision/datasets/ADE20K/]
  17. INRIA Annotations for Graz-02[http://lear.inrialpes.fr/people/marszalek/data/ig02/]

比赛

  1. MSRC-21 [http://rodrigob.github.io/are_we_there_yet/build/semantic_labeling_datasets_results.html]
  2. Cityscapes [https://www.cityscapes-dataset.com/benchmarks/]
  3. VOC2012 [http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=6]

领域专家

  1. Jonathan Long
  2. Liang-Chieh Chen
  3. Hyeonwoo Noh
  4. Bharath Hariharan
  5. Fisher Yu
  6. Vijay Badrinarayanan
  7. Guosheng Lin

初步版本，水平有限，有错误或者不完善的地方，欢迎大家提建议和补充，会一直保持更新，本文为专知内容组原创内容，未经允许不得转载，如需转载请发送邮件至fangquanyi@gmail.com 或 联系微信专知小助手（Rancho_Fang）

敬请关注http://www.zhuanzhi.ai 和关注专知公众号，获取第一手AI相关知识

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图像分割 ·
11 月 22 日
图像分割方法综述
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为了解图像分割领域的研究现状，对图像分割方法进行了系统性梳理，首先按照基于阈值、边缘、区域、聚类、图论及特定理论等6类方法介绍传统图像分割方法；然后介绍基于深度学习的分割方法，并探讨了几种常用的分割网络模型，包括全卷积网络（full convolutional network,FCN）、金字塔场景解析网络（pyramid scene parsing network,PSPNet）、DeepLab、Mask R-CNN；最后在图像分割的常用数据集上对同类方法进行了性能比较和分析。

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图像分割 · Automator · 深度学习框架 · 泛化理论 · CASES ·
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A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysis
Mahendra Khened,Avinash Kori,Haran Rajkumar,Balaji Srinivasan,Ganapathy Krishnamurthi
Mahendra Khened,Avinash Kori,Haran Rajkumar,Balaji Srinivasan,Ganapathy Krishnamurthi

Histopathology tissue analysis is considered the gold standard in cancer diagnosis and prognosis. Given the large size of these images and the increase in the number of potential cancer cases, an automated solution as an aid to histopathologists is highly desirable. In the recent past, deep learning-based techniques have provided state of the art results in a wide variety of image analysis tasks, including analysis of digitized slides. However, the size of images and variability in histopathology tasks makes it a challenge to develop an integrated framework for histopathology image analysis. We propose a deep learning-based framework for histopathology tissue analysis. We demonstrate the generalizability of our framework, including training and inference, on several open-source datasets, which include CAMELYON (breast cancer metastases), DigestPath (colon cancer), and PAIP (liver cancer) datasets. We discuss multiple types of uncertainties pertaining to data and model, namely aleatoric and epistemic, respectively. Simultaneously, we demonstrate our model generalization across different data distribution by evaluating some samples on TCGA data. On CAMELYON16 test data (n=139) for the task of lesion detection, the FROC score achieved was 0.86 and in the CAMELYON17 test-data (n=500) for the task of pN-staging the Cohen's kappa score achieved was 0.9090 (third in the open leaderboard). On DigestPath test data (n=212) for the task of tumor segmentation, a Dice score of 0.782 was achieved (fourth in the challenge). On PAIP test data (n=40) for the task of viable tumor segmentation, a Jaccard Index of 0.75 (third in the challenge) was achieved, and for viable tumor burden, a score of 0.633 was achieved (second in the challenge). Our entire framework and related documentation are freely available at GitHub and PyPi.

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最新论文

图像分割 · Automator · 深度学习框架 · 泛化理论 · CASES ·
11 月 18 日
A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysis
Mahendra Khened,Avinash Kori,Haran Rajkumar,Balaji Srinivasan,Ganapathy Krishnamurthi
Mahendra Khened,Avinash Kori,Haran Rajkumar,Balaji Srinivasan,Ganapathy Krishnamurthi

Histopathology tissue analysis is considered the gold standard in cancer diagnosis and prognosis. Given the large size of these images and the increase in the number of potential cancer cases, an automated solution as an aid to histopathologists is highly desirable. In the recent past, deep learning-based techniques have provided state of the art results in a wide variety of image analysis tasks, including analysis of digitized slides. However, the size of images and variability in histopathology tasks makes it a challenge to develop an integrated framework for histopathology image analysis. We propose a deep learning-based framework for histopathology tissue analysis. We demonstrate the generalizability of our framework, including training and inference, on several open-source datasets, which include CAMELYON (breast cancer metastases), DigestPath (colon cancer), and PAIP (liver cancer) datasets. We discuss multiple types of uncertainties pertaining to data and model, namely aleatoric and epistemic, respectively. Simultaneously, we demonstrate our model generalization across different data distribution by evaluating some samples on TCGA data. On CAMELYON16 test data (n=139) for the task of lesion detection, the FROC score achieved was 0.86 and in the CAMELYON17 test-data (n=500) for the task of pN-staging the Cohen's kappa score achieved was 0.9090 (third in the open leaderboard). On DigestPath test data (n=212) for the task of tumor segmentation, a Dice score of 0.782 was achieved (fourth in the challenge). On PAIP test data (n=40) for the task of viable tumor segmentation, a Jaccard Index of 0.75 (third in the challenge) was achieved, and for viable tumor burden, a score of 0.633 was achieved (second in the challenge). Our entire framework and related documentation are freely available at GitHub and PyPi.

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