Prediction of Tuberculosis using U-Net and segmentation techniques

One of the most serious public health problems in Peru and worldwide is Tuberculosis (TB), which is produced by a bacterium known as Mycobacterium tuberculosis. The purpose of this work is to facilitate and automate the diagnosis of tuberculosis using the MODS method and using lens-free microscopy, as it is easier to calibrate and easier to use by untrained personnel compared to lens microscopy. Therefore, we employed a U-Net network on our collected data set to perform automatic segmentation of cord shape bacterial accumulation and then predict tuberculosis. Our results show promising evidence for automatic segmentation of TB cords, and thus good accuracy for TB prediction.

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

[2]  Edouard A Hay,et al.  Performance of convolutional neural networks for identification of bacteria in 3D microscopy datasets , 2018, bioRxiv.

[3]  Nima Tajbakhsh,et al.  UNet++: A Nested U-Net Architecture for Medical Image Segmentation , 2018, DLMIA/ML-CDS@MICCAI.

[4]  Qi Yang,et al.  Intracranial Vessel Wall Segmentation Using Convolutional Neural Networks , 2019, IEEE Transactions on Biomedical Engineering.

[5]  Thomas Brox,et al.  U-Net: deep learning for cell counting, detection, and morphometry , 2018, Nature Methods.

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

[7]  Derek C. Rose,et al.  Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier] , 2010, IEEE Computational Intelligence Magazine.

[8]  Bogdan Kwolek,et al.  Face Detection Using Convolutional Neural Networks and Gabor Filters , 2005, ICANN.

[9]  L. Gabbasova,et al.  Global tuberculosis report (2014) , 2014 .

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

[11]  R. Gilman,et al.  Implementación de un sistema de telediagnóstico de tuberculosis y determinación de multidrogorresistencia basada en el método Mods en Trujillo, Perú , 2014 .

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

[13]  Cristian A. Linte,et al.  A Modified U-Net Convolutional Network Featuring a Nearest-neighbor Re-sampling-based Elastic-Transformation for Brain Tissue Characterization and Segmentation , 2018, 2018 IEEE Western New York Image and Signal Processing Workshop (WNYISPW).