Lesion-Inspired Denoising Network: Connecting Medical Image Denoising and Lesion Detection

Deep learning has achieved notable performance in the denoising task of low-quality medical images and the detection task of lesions, respectively. However, existing low-quality medical image denoising approaches are disconnected from the detection task of lesions. Intuitively, the quality of denoised images will influence the lesion detection accuracy that in turn can be used to affect the denoising performance. To this end, we propose a play-and-plug medical image denoising framework, namely Lesion-Inspired Denoising Network (LIDnet), to collaboratively improve both denoising performance and detection accuracy of denoised medical images. Specifically, we propose to insert the feedback of downstream detection task into existing denoising framework by jointly learning a multi-loss objective. Instead of using perceptual loss calculated on the entire feature map, a novel region-of-interest (ROI) perceptual loss induced by the lesion detection task is proposed to further connect these two tasks. To achieve better optimization for overall framework, we propose a customized collaborative training strategy for LIDnet. On consideration of clinical usability and imaging characteristics, three low-dose CT images datasets are used to evaluate the effectiveness of the proposed LIDnet. Experiments show that, by equipping with LIDnet, both of the denoising and lesion detection performance of baseline methods can be significantly improved.

[1]  Haoliang Li,et al.  Low-Dose CT Image Blind Denoising with Graph Convolutional Networks , 2020, ICONIP.

[2]  Zhengrong Liang,et al.  Sinogram noise reduction for low-dose CT by statistics-based nonlinear filters , 2005, SPIE Medical Imaging.

[3]  W. Kalender,et al.  Evaluation of section sensitivity profiles and image noise in spiral CT. , 1992, Radiology.

[4]  Jason Cong,et al.  SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss Network , 2020, IEEE Transactions on Medical Imaging.

[5]  Xuanqin Mou,et al.  Tensor-Based Dictionary Learning for Spectral CT Reconstruction , 2017, IEEE Transactions on Medical Imaging.

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

[7]  Ge Wang,et al.  Low-Dose CT Image Denoising Using a Generative Adversarial Network With a Hybrid Loss Function for Noise Learning , 2020, IEEE Access.

[8]  Yan Liu,et al.  Low-dose CT restoration via stacked sparse denoising autoencoders , 2018, Neurocomputing.

[9]  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.

[10]  Gumersindo Verdú Martín,et al.  Medical image restoration with different types of noise , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  C. McCollough,et al.  Relationship between noise, dose, and pitch in cardiac multi-detector row CT. , 2006, Radiographics : a review publication of the Radiological Society of North America, Inc.

[12]  Mouaz H. Al-Mallah,et al.  Routine low-radiation-dose coronary computed tomography angiography , 2014 .

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

[14]  Patrick Le Callet,et al.  Diagnostic quality assessment of medical images: Challenges and trends , 2010, 2010 2nd European Workshop on Visual Information Processing (EUVIP).

[15]  J. Jan.  Medical Image Processing, Reconstruction and Restoration: Concepts and Methods , 2005 .

[16]  Saeed Mozaffari,et al.  CNN adversarial attack mitigation using perturbed samples training , 2021, Multimedia Tools and Applications.

[17]  Uwe Kruger,et al.  Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction , 2019, Nat. Mach. Intell..

[18]  Qi Tian,et al.  CenterNet: Keypoint Triplets for Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[19]  Shuai Leng,et al.  Low Dose CT Image and Projection Dataset. , 2020, Medical physics.

[20]  K. Vengatesan,et al.  Design of cognitive image filters for suppression of noise level in medical images , 2019, Measurement.

[21]  Kaushik Roy,et al.  Going Deeper in Spiking Neural Networks: VGG and Residual Architectures , 2018, Front. Neurosci..

[22]  Hu Chen,et al.  Low-dose CT via convolutional neural network. , 2017, Biomedical optics express.

[23]  K. P. Kim,et al.  Radiation exposure from CT scans in childhood and subsequent risk of leukaemia and brain tumours: a retrospective cohort study , 2012, The Lancet.

[24]  Javad Alirezaie,et al.  Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer , 2019, Journal of Digital Imaging.

[25]  Léon Bottou,et al.  Wasserstein Generative Adversarial Networks , 2017, ICML.

[26]  Daniel Cohen-Or,et al.  Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Xuanqin Mou,et al.  Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss , 2017, IEEE Transactions on Medical Imaging.

[28]  S. Wijewickrema,et al.  A deep learning based framework for the registration of three dimensional multi-modal medical images of the head , 2021, Scientific Reports.

[29]  Cynthia M. McCollough,et al.  Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT. , 2009, Medical physics.

[30]  Joachim Hornegger,et al.  Ray Contribution Masks for Structure Adaptive Sinogram Filtering , 2012, IEEE Transactions on Medical Imaging.

[31]  Feng Lin,et al.  Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network , 2017, IEEE Transactions on Medical Imaging.

[32]  Matthew B. Blaschko,et al.  An ensemble deep learning based approach for red lesion detection in fundus images , 2017, Comput. Methods Programs Biomed..

[33]  Nima Tajbakhsh,et al.  Computer-aided detection and visualization of pulmonary embolism using a novel, compact, and discriminative image representation , 2019, Medical Image Anal..

[34]  Jan Nordin,et al.  Improving Diagnostic Viewing of Medical Images using Enhancement Algorithms , 2011 .

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

[36]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[37]  Ge Wang,et al.  Structurally-Sensitive Multi-Scale Deep Neural Network for Low-Dose CT Denoising , 2018, IEEE Access.

[38]  Mohammad H. Jafari,et al.  Skin lesion segmentation in clinical images using deep learning , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[39]  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).

[40]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[41]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[42]  Andriy Fedorov,et al.  Computational Radiomics System to Decode the Radiographic Phenotype. , 2017, Cancer research.

[43]  K. Stierstorfer,et al.  Image reconstruction and image quality evaluation for a 64-slice CT scanner with z-flying focal spot. , 2005, Medical physics.

[44]  Aaron C. Courville,et al.  Improved Training of Wasserstein GANs , 2017, NIPS.

[45]  Hongming Shan,et al.  3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network , 2018, IEEE Transactions on Medical Imaging.

[46]  Yugyung Lee,et al.  Utilizing Mask R-CNN for Detection and Segmentation of Oral Diseases , 2018, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[47]  K. Shankar,et al.  Deep learning based an automated skin lesion segmentation and intelligent classification model , 2020, Journal of Ambient Intelligence and Humanized Computing.

[48]  Xianming Liu,et al.  Connecting Image Denoising and High-Level Vision Tasks via Deep Learning , 2018, IEEE Transactions on Image Processing.

[49]  Min Zhou,et al.  Probability-based Mask R-CNN for pulmonary embolism detection , 2021, Neurocomputing.

[50]  Zhengrong Liang,et al.  Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstruction , 2012, Physics in medicine and biology.

[51]  Le Lu,et al.  DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning , 2018, Journal of medical imaging.