Three Dimensional Nuclei Segmentation and Classification of Fluorescence Microscopy Images

Segmentation and classification of cell nuclei in fluorescence 3D microscopy image volumes are fundamental steps for image analysis. However, accurate cell nuclei segmentation and detection in microscopy image volumes are hampered by poor image quality, crowding of nuclei, and large variation in nuclei size and shape. In this paper, we present an unsupervised volume to volume translation approach adapted from the Recycle-GAN using modified Hausdorff distance loss for synthetically generating nuclei with better shapes. A 3D CNN with a regularization term is used for nuclei segmentation and classification followed by nuclei boundary refinement. Experimental results demonstrate that the proposed method can successfully segment nuclei and identify individual nuclei.

[1]  Septimiu E. Salcudean,et al.  Reducing the Hausdorff Distance in Medical Image Segmentation With Convolutional Neural Networks , 2019, IEEE Transactions on Medical Imaging.

[2]  M. Unser,et al.  The colored revolution of bioimaging , 2006, IEEE Signal Processing Magazine.

[3]  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).

[4]  Yaser Sheikh,et al.  Recycle-GAN: Unsupervised Video Retargeting , 2018, ECCV.

[5]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[6]  Edward J. Delp,et al.  Nuclei Segmentation of Fluorescence Microscopy Images Using Three Dimensional Convolutional Neural Networks , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[7]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[9]  Edward J. Delp,et al.  Three Dimensional Blind Image Deconvolution for Fluorescence Microscopy using Generative Adversarial Networks , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[10]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.

[11]  Edward J. Delp,et al.  Tubule segmentation of fluorescence microscopy images based on convolutional neural networks with inhomogeneity correction , 2018, Computational Imaging.

[12]  E. Meijering.  Cell Segmentation : 50 Years Down the Road , 2012 .

[13]  Edward J. Delp,et al.  Nuclei counting in microscopy images with three dimensional generative adversarial networks , 2019, Medical Imaging: Image Processing.

[14]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[15]  拓海 杉山,et al.  “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .

[16]  Edward J. Delp,et al.  Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[17]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Edward J. Delp,et al.  Nuclei segmentation of fluorescence microscopy images using convolutional neural networks , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[19]  Anne E Carpenter,et al.  CellProfiler: image analysis software for identifying and quantifying cell phenotypes , 2006, Genome Biology.

[20]  Edward J. Delp,et al.  Locating Objects Without Bounding Boxes , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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

[22]  Shunxing Bao,et al.  Adversarial synthesis learning enables segmentation without target modality ground truth , 2017, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[23]  Lin Yang,et al.  Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[24]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[25]  Edward J. Delp,et al.  Segmentation of fluorescence microscopy images using three dimensional active contours with inhomogeneity correction , 2017, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[26]  Thomas Brox,et al.  3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.

[27]  Vijayan K. Asari,et al.  Microscopic Nuclei Classification, Segmentation and Detection with improved Deep Convolutional Neural Network (DCNN) Approaches , 2018, ArXiv.

[28]  Reto Meuli,et al.  A Neu Brain Segmentation Framework. Proceedings of the Medical Image Computing and Computer-Assisted Intervention. , 2003 .

[29]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..