Mark-Guided Segmentation of Ultrasonic Thyroid Nodules Using Deep Learning

Segmentation of thyroid nodules in the ultrasound image is a chal lenging task not only because of the speckle noise in ultrasound images but also the heterogeneous appearance and blurry bound aries of thyroid nodules. In this paper, we apply U-Net, a fully convolutional neural network, to thyroid nodule segmentation, and further proposed an interactive segmentation method based on it and the guidance of annotation marks. Firstly, the four end-points of the major and minor axes of a nodule are determined manually. Then, four white spots are directly drawn at the four points on the image to guide the training and inference of the deep neural network. Our method is evaluated on a dataset composed of 900 ultrasound thyroid images. The experimental results indicate that our mark-guided segmentation method is able to delineate nodules accurately with little human intervention and achieve a remarkable improvement over its automatic counterpart.

[1]  Luca Maria Gambardella,et al.  Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images , 2012, NIPS.

[2]  Fa Wu,et al.  Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks , 2017, International Journal of Computer Assisted Radiology and Surgery.

[3]  S. Osher,et al.  Algorithms Based on Hamilton-Jacobi Formulations , 1988 .

[4]  Dorin Comaniciu,et al.  Database-guided breast tumor detection and segmentation in 2D ultrasound images , 2010, Medical Imaging.

[5]  Michalis A. Savelonas,et al.  A genetically optimized level set approach to segmentation of thyroid ultrasound images , 2007, Applied Intelligence.

[6]  Yan Xu,et al.  A modified spatial fuzzy clustering method based on texture analysis for ultrasound image segmentation , 2009, 2009 IEEE International Symposium on Industrial Electronics.

[7]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

[8]  Yaozong Gao,et al.  Fully convolutional networks for multi-modality isointense infant brain image segmentation , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[9]  Joseph F. Murray,et al.  Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation , 2010, Neural Computation.

[10]  Nikos Dimitropoulos,et al.  A variable background active contour model for automatic detection of thyroid nodules in ultrasound images , 2005, IEEE International Conference on Image Processing 2005.

[11]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Nam Chul Kim,et al.  RD-Based Seeded Region Growing for Extraction of Breast Tumor in an Ultrasound Volume , 2005, CIS.

[13]  Marleen de Bruijne,et al.  GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network , 2017, MICCAI.

[14]  Chuan-Yu Chang,et al.  Thyroid segmentation and volume estimation in ultrasound images , 2010, 2008 IEEE International Conference on Systems, Man and Cybernetics.

[15]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[16]  Luca Persani,et al.  Standardized Ultrasound Report for Thyroid Nodules: The Endocrinologist's Viewpoint , 2013, European Thyroid Journal.

[17]  Michalis A. Savelonas,et al.  Segmentation of Medical Images with Regional Inhomogeneities , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[18]  Jiangwen Deng,et al.  A fast level set method for segmentation of low contrast noisy biomedical images , 2002, Pattern Recognit. Lett..

[19]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

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

[21]  Dimitrios K. Iakovidis,et al.  Efficient and Effective Ultrasound Image Analysis Scheme for Thyroid Nodule Detection , 2007, ICIAR.

[22]  Luca Maria Gambardella,et al.  Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks , 2013, MICCAI.

[23]  Samuel Kadoury,et al.  Liver lesion segmentation informed by joint liver segmentation , 2017, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[24]  Savita Gupta,et al.  Computer aided thyroid nodule detection system using medical ultrasound images , 2018, Biomed. Signal Process. Control..

[25]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[26]  Elli Angelopoulou,et al.  Using Power Watersheds to Segment Benign Thyroid Nodules in Ultrasound Image Data , 2011, Bildverarbeitung für die Medizin.

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

[28]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.