A Convolutional Neural Network Combining Discriminative Dictionary Learning and Sequence Tracking for Left Ventricular Detection

Cardiac MRI left ventricular (LV) detection is frequently employed to assist cardiac registration or segmentation in computer-aided diagnosis of heart diseases. Focusing on the challenging problems in LV detection, such as the large span and varying size of LV areas in MRI, as well as the heterogeneous myocardial and blood pool parts in LV areas, a convolutional neural network (CNN) detection method combining discriminative dictionary learning and sequence tracking is proposed in this paper. To efficiently represent the different sub-objects in LV area, the method deploys discriminant dictionary to classify the superpixel oversegmented regions, then the target LV region is constructed by label merging and multi-scale adaptive anchors are generated in the target region for handling the varying sizes. Combining with non-differential anchors in regional proposal network, the left ventricle object is localized by the CNN based regression and classification strategy. In order to solve the problem of slow classification speed of discriminative dictionary, a fast generation module of left ventricular scale adaptive anchors based on sequence tracking is also proposed on the same individual. The method and its variants were tested on the heart atlas data set. Experimental results verified the effectiveness of the proposed method and according to some evaluation indicators, it obtained 92.95% in AP50 metric and it was the most competitive result compared to typical related methods. The combination of discriminative dictionary learning and scale adaptive anchor improves adaptability of the proposed algorithm to the varying left ventricular areas. This study would be beneficial in some cardiac image processing such as region-of-interest cropping and left ventricle volume measurement.

[1]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[2]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Xiaohui Xie,et al.  DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification , 2018, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

[4]  D. Dey,et al.  Fully Automated CT Quantification of Epicardial Adipose Tissue by Deep Learning: A Multicenter Study. , 2019, Radiology. Artificial intelligence.

[5]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Ross B. Girshick,et al.  Mask R-CNN , 2017, 1703.06870.

[7]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Hei Law,et al.  CornerNet: Detecting Objects as Paired Keypoints , 2018, ECCV.

[9]  Laura Diosan,et al.  Region-of-Interest-Based Cardiac Image Segmentation with Deep Learning , 2021, Applied Sciences.

[10]  Bram van Ginneken,et al.  Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images , 2016, IEEE Transactions on Medical Imaging.

[11]  Constantine Bekas,et al.  BAGAN: Data Augmentation with Balancing GAN , 2018, ArXiv.

[12]  Lars Petersson,et al.  DeNet: Scalable Real-Time Object Detection with Directed Sparse Sampling , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[13]  Nuno Vasconcelos,et al.  Cascade R-CNN: Delving Into High Quality Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[14]  Shin'ichi Satoh,et al.  Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images , 2019, CIKM.

[15]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Michael Felsberg,et al.  Accurate Scale Estimation for Robust Visual Tracking , 2014, BMVC.

[17]  Marwa M. A. Hadhoud,et al.  Automatic global localization of the heart from Cine MRI images , 2011, 2011 IEEE International Symposium on IT in Medicine and Education.

[18]  Naif Alajlan,et al.  Two-Stage Mask-RCNN Approach for Detecting and Segmenting the Optic Nerve Head, Optic Disc, and Optic Cup in Fundus Images , 2020, Applied Sciences.

[19]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Yanmin Niu,et al.  Myocardium Detection by Deep SSAE Feature and Within-Class Neighborhood Preserved Support Vector Classifier and Regressor , 2019, Sensors.

[21]  Dilber Uzun Ozsahin,et al.  Sliding Window Based Machine Learning System for the Left Ventricle Localization in MR Cardiac Images , 2017, Appl. Comput. Intell. Soft Comput..

[22]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Lei Zhang,et al.  Metaface learning for sparse representation based face recognition , 2010, 2010 IEEE International Conference on Image Processing.

[24]  Jian Sun,et al.  Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[26]  Massimo Midiri,et al.  A semi-automatic approach for epicardial adipose tissue segmentation and quantification on cardiac CT scans , 2019, Comput. Biol. Medicine.

[27]  Р Ю Чуйков,et al.  Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector , 2017 .

[28]  Marios Savvides,et al.  Feature Selective Anchor-Free Module for Single-Shot Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.

[30]  Alistair A. Young,et al.  The Cardiac Atlas Project—an imaging database for computational modeling and statistical atlases of the heart , 2011, Bioinform..

[31]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[33]  Katja Bühler,et al.  Fast and Robust Localization of the Heart in Cardiac MRI Series - a Cascade of Operations for Automatically Detecting the Heart in Cine MRI Series , 2008, VISAPP.

[34]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[35]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[36]  Aimin Hao,et al.  A CADe system for nodule detection in thoracic CT images based on artificial neural network , 2017, Science China Information Sciences.

[37]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Ioannis A. Kakadiaris,et al.  Localization and Segmentation of Left Ventricle in Cardiac Cine-MR Images , 2009, IEEE Transactions on Biomedical Engineering.

[39]  Kavita Bala,et al.  Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[40]  Xingyi Zhou,et al.  Objects as Points , 2019, ArXiv.

[41]  Shifeng Zhang,et al.  Single-Shot Refinement Neural Network for Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[42]  Yi Li,et al.  R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.

[43]  David Zhang,et al.  Fisher Discrimination Dictionary Learning for sparse representation , 2011, 2011 International Conference on Computer Vision.

[44]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[45]  Shaoting Zhang,et al.  Recognizing End-Diastole and End-Systole Frames via Deep Temporal Regression Network , 2016, MICCAI.

[46]  Simon K. Warfield,et al.  Left Ventricular Segmentation Challenge from Cardiac MRI: A Collation Study , 2011, STACOM.

[47]  Abeer Alsadoon,et al.  A novel solution of using deep learning for left ventricle detection: Enhanced feature extraction , 2020, Comput. Methods Programs Biomed..