Segmentation of Drosophila Heart in Optical Coherence Microscopy Images Using Convolutional Neural Networks

نویسندگان

  • Lian Duan
  • Xi Qin
  • Yuanhao He
  • Xialin Sang
  • Jinda Pan
  • Tao Xu
  • Jing Men
  • Rudolph E. Tanzi
  • Airong Li
  • Yutao Ma
  • Chao Zhou
چکیده

Convolutional neural networks are powerful tools for image segmentation and classification. Here, we use this method to identify and mark the heart region of Drosophila at different developmental stages in the cross-sectional images acquired by a custom optical coherence microscopy (OCM) system. With our well-trained convolutional neural network model, the heart regions through multiple heartbeat cycles can be marked with an intersection over union (IOU) of ~86%. Various morphological and dynamical cardiac parameters can be quantified accurately with automatically segmented heart regions. This study demonstrates an efficient heart segmentation method to analyze OCM images of the beating heart in Drosophila. References and links 1. D. Huang, E. Swanson, C. Lin, J. Schuman, W. Stinson, W. Chang, M. Hee, T. Flotte, K. Gregory, C. Puliafito, and a. et, "Optical coherence tomography," Science 254, 1178-1181 (1991). 2. W. Drexler, M. Liu, A. Kumar, T. Kamali, A. Unterhuber, and R. A. Leitgeb, "Optical coherence tomography today: speed, contrast, and multimodality," Journal of Biomedical Optics 19, 071412 (2014). 3. J. Fujimoto and E. Swanson, "The Development, Commercialization, and Impact of Optical Coherence Tomography," Investigative Ophthalmology & Visual Science 57, OCT1-OCT13 (2016). 4. M. Wojtkowski, "High-speed optical coherence tomography: basics and applications," Appl. Opt. 49, D30-D61 (2010). 5. A. D. Aguirre, C. Zhou, H.-C. Lee, O. O. Ahsen, and J. G. Fujimoto, "Optical Coherence Microscopy," in Optical Coherence Tomography: Technology and Applications, W. Drexler and J. G. Fujimoto, eds. (Springer International Publishing, Cham, 2015), pp. 865-911. 6. J. A. Izatt, M. R. Hee, G. M. Owen, E. A. Swanson, and J. G. Fujimoto, "Optical coherence microscopy in scattering media," Opt. Lett. 19, 590-592 (1994). 7. L. Kagemann, H. Ishikawa, J. Zou, P. Charukamnoetkanok, G. Wollstein, K. A. Townsend, M. L. Gabriele, N. Bahary, X. Wei, J. G. Fujimoto, and J. S. Schuman, "Repeated, noninvasive, high resolution spectral domain optical coherence tomography imaging of zebrafish embryos," Molecular Vision 14,

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A multi-scale convolutional neural network for automatic cloud and cloud shadow detection from Gaofen-1 images

The reconstruction of the information contaminated by cloud and cloud shadow is an important step in pre-processing of high-resolution satellite images. The cloud and cloud shadow automatic segmentation could be the first step in the process of reconstructing the information contaminated by cloud and cloud shadow. This stage is a remarkable challenge due to the relatively inefficient performanc...

متن کامل

Integration of Deep Learning Algorithms and Bilateral Filters with the Purpose of Building Extraction from Mono Optical Aerial Imagery

The problem of extracting the building from mono optical aerial imagery with high spatial resolution is always considered as an important challenge to prepare the maps. The goal of the current research is to take advantage of the semantic segmentation of mono optical aerial imagery to extract the building which is realized based on the combination of deep convolutional neural networks (DCNN) an...

متن کامل

Estimation of Hand Skeletal Postures by Using Deep Convolutional Neural Networks

Hand posture estimation attracts researchers because of its many applications. Hand posture recognition systems simulate the hand postures by using mathematical algorithms. Convolutional neural networks have provided the best results in the hand posture recognition so far. In this paper, we propose a new method to estimate the hand skeletal posture by using deep convolutional neural networks. T...

متن کامل

Cystoscopy Image Classication Using Deep Convolutional Neural Networks

In the past three decades, the use of smart methods in medical diagnostic systems has attractedthe attention of many researchers. However, no smart activity has been provided in the eld ofmedical image processing for diagnosis of bladder cancer through cystoscopy images despite the highprevalence in the world. In this paper, two well-known convolutional neural networks (CNNs) ...

متن کامل

Cystoid macular edema segmentation of Optical Coherence Tomography images using fully convolutional neural networks and fully connected CRFs

In this paper we present a new method for cystoid macular edema (CME) segmentation in retinal Optical Coherence Tomography (OCT) images, using a fully convolutional neural network (FCN) and a fully connected conditional random fields (dense CRFs). As a first step, the framework trains the FCN model to extract features from retinal layers in OCT images, which exhibit CME, and then segments CME r...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2018