Palash Ghosal1, Tamal Chowdhury2, Amish Kumar3, Ashok Kumar Bhadra4, Jayasree Chakraborty5, Debashis Nandi6. 1. Department of Computer Science and Engineering, National Institute of Technology Durgapur-713209, West Bengal, India. Electronic address: ghosalpalash@gmail.com. 2. Department of Electronics and Communication Engineering, National Institute of Technology Durgapur-713209, West Bengal, India. Electronic address: tgchowdhury101@gmail.com. 3. Department of Computer Science and Engineering, National Institute of Technology Durgapur-713209, West Bengal, India. Electronic address: amishkumar562@gmail.com. 4. Department of Radiology, KPC Medical College and Hospital, Jadavpur, 700032, West Bengal, India. Electronic address: akrbhadra@gmail.com. 5. Department of Hepatopancreatobiliary Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. Electronic address: jayasree2@gmail.com. 6. Department of Computer Science and Engineering, National Institute of Technology Durgapur-713209, West Bengal, India. Electronic address: debashis@cse.nitdgp.ac.in.
Abstract
BACKGROUND AND OBJECTIVES: Accurate segmentation of critical tissues from a brain MRI is pivotal for characterization and quantitative pattern analysis of the human brain and thereby, identifies the earliest signs of various neurodegenerative diseases. To date, in most cases, it is done manually by the radiologists. The overwhelming workload in some of the thickly populated nations may cause exhaustion leading to interruption for the doctors, which may pose a continuing threat to patient safety. A novel fusion method called U-Net inception based on 3D convolutions and transition layers is proposed to address this issue. METHODS: A 3D deep learning method called Multi headed U-Net with Residual Inception (MhURI) accompanied by Morphological Gradient channel for brain tissue segmentation is proposed, which incorporates Residual Inception 2-Residual (RI2R) module as the basic building block. The model exploits the benefits of morphological pre-processing for structural enhancement of MR images. A multi-path data encoding pipeline is introduced on top of the U-Net backbone, which encapsulates initial global features and captures the information from each MRI modality. RESULTS: The proposed model has accomplished encouraging outcomes, which appreciates the adequacy in terms of some of the established quality metrices when compared with some of the state-of-the-art methods while evaluating with respect to two popular publicly available data sets. CONCLUSION: The model is entirely automatic and able to segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) from brain MRI effectively with sufficient accuracy. Hence, it may be considered to be a potential computer-aided diagnostic (CAD) tool for radiologists and other medical practitioners in their clinical diagnosis workflow.
BACKGROUND AND OBJECTIVES: Accurate segmentation of critical tissues from a brain MRI is pivotal for characterization and quantitative pattern analysis of the human brain and thereby, identifies the earliest signs of various neurodegenerative diseases. To date, in most cases, it is done manually by the radiologists. The overwhelming workload in some of the thickly populated nations may cause exhaustion leading to interruption for the doctors, which may pose a continuing threat to patient safety. A novel fusion method called U-Net inception based on 3D convolutions and transition layers is proposed to address this issue. METHODS: A 3D deep learning method called Multi headed U-Net with Residual Inception (MhURI) accompanied by Morphological Gradient channel for brain tissue segmentation is proposed, which incorporates Residual Inception 2-Residual (RI2R) module as the basic building block. The model exploits the benefits of morphological pre-processing for structural enhancement of MR images. A multi-path data encoding pipeline is introduced on top of the U-Net backbone, which encapsulates initial global features and captures the information from each MRI modality. RESULTS: The proposed model has accomplished encouraging outcomes, which appreciates the adequacy in terms of some of the established quality metrices when compared with some of the state-of-the-art methods while evaluating with respect to two popular publicly available data sets. CONCLUSION: The model is entirely automatic and able to segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) from brain MRI effectively with sufficient accuracy. Hence, it may be considered to be a potential computer-aided diagnostic (CAD) tool for radiologists and other medical practitioners in their clinical diagnosis workflow.
Authors: Pim Moeskops; Max A Viergever; Adrienne M Mendrik; Linda S de Vries; Manon J N L Benders; Ivana Isgum Journal: IEEE Trans Med Imaging Date: 2016-03-30 Impact factor: 10.048
Authors: Pablo Ribalta Lorenzo; Jakub Nalepa; Barbara Bobek-Billewicz; Pawel Wawrzyniak; Grzegorz Mrukwa; Michal Kawulok; Pawel Ulrych; Michael P Hayball Journal: Comput Methods Programs Biomed Date: 2019-05-11 Impact factor: 5.428
Authors: Tobias Heimann; Bram van Ginneken; Martin A Styner; Yulia Arzhaeva; Volker Aurich; Christian Bauer; Andreas Beck; Christoph Becker; Reinhard Beichel; György Bekes; Fernando Bello; Gerd Binnig; Horst Bischof; Alexander Bornik; Peter M M Cashman; Ying Chi; Andrés Cordova; Benoit M Dawant; Márta Fidrich; Jacob D Furst; Daisuke Furukawa; Lars Grenacher; Joachim Hornegger; Dagmar Kainmüller; Richard I Kitney; Hidefumi Kobatake; Hans Lamecker; Thomas Lange; Jeongjin Lee; Brian Lennon; Rui Li; Senhu Li; Hans-Peter Meinzer; Gábor Nemeth; Daniela S Raicu; Anne-Mareike Rau; Eva M van Rikxoort; Mikaël Rousson; László Rusko; Kinda A Saddi; Günter Schmidt; Dieter Seghers; Akinobu Shimizu; Pieter Slagmolen; Erich Sorantin; Grzegorz Soza; Ruchaneewan Susomboon; Jonathan M Waite; Andreas Wimmer; Ivo Wolf Journal: IEEE Trans Med Imaging Date: 2009-02-10 Impact factor: 10.048