Book ChapterDOI
Brain Tumor Segmentation Using Dense Fully Convolutional Neural Network
Mazhar Shaikh,Ganesh Anand,Gagan Acharya,Abhijit Amrutkar,Varghese Alex,Ganapathy Krishnamurthi +5 more
- pp 309-319
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TLDR
The usage of the 100 layer Tiramisu architecture for the segmentation of brain tumor from multi modal MR images, which is evolved by integrating a densely connected fully convolutional neural network (FCNN), followed by post-processing using a Dense Conditional Random Field (DCRF).Abstract:
Manual segmentation of brain tumor is often time consuming and the performance of the segmentation varies based on the operators experience. This leads to the requisition of a fully automatic method for brain tumor segmentation. In this paper, we propose the usage of the 100 layer Tiramisu architecture for the segmentation of brain tumor from multi modal MR images, which is evolved by integrating a densely connected fully convolutional neural network (FCNN), followed by post-processing using a Dense Conditional Random Field (DCRF). The network consists of blocks of densely connected layers, transition down layers in down-sampling path and transition up layers in up-sampling path. The method was tested on dataset provided by Multi modal Brain Tumor Segmentation Challenge (BraTS) 2017. The training data is composed of 210 high-grade brain tumor and 74 low-grade brain tumor cases. The proposed network achieves a mean whole tumor, tumor core & active tumor dice score of 0.87, 0.68 & 0.65. Respectively on the BraTS ’17 validation set and 0.83, 0.65 & 0.65 on the Brats ’17 test set.read more
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Posted ContentDOI
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Spyridon Bakas,Mauricio Reyes,Andras Jakab,Stefan Bauer,Markus Rempfler,Alessandro Crimi,Russell T. Shinohara,Christoph Berger,Sung Min Ha,Martin Rozycki,Marcel Prastawa,Esther Alberts,Jana Lipkova,John Freymann,John Freymann,Justin Kirby,Justin Kirby,Michel Bilello,Hassan M. Fathallah-Shaykh,Roland Wiest,Jan S. Kirschke,Benedikt Wiestler,Rivka R. Colen,Aikaterini Kotrotsou,Pamela LaMontagne,Daniel S. Marcus,Mikhail Milchenko,Arash Nazeri,Marc-André Weber,Abhishek Mahajan,Ujjwal Baid,Elizabeth R. Gerstner,Dongjin Kwon,Gagan Acharya,Manu Agarwal,M. S. Alam,Alberto Albiol,Antonio Albiol,F. Albiol,Varghese Alex,Nigel M. Allinson,Pedro H. A. Amorim,Abhijit Amrutkar,Ganesh Anand,Simon Andermatt,Tal Arbel,Pablo Arbeláez,Aaron Avery,Muneeza Azmat,B. Pranjal,Wenjia Bai,Subhashis Banerjee,Subhashis Banerjee,Bill Barth,Thomas Batchelder,Kayhan Batmanghelich,Enzo Battistella,Enzo Battistella,Andrew Beers,Mikhail Belyaev,Martin Bendszus,Eze Benson,Jose Bernal,H. N. Bharath,George Biros,Sotirios Bisdas,James M. Brown,Mariano Cabezas,Shilei Cao,Jorge Cardoso,Eric Carver,Adrià Casamitjana,Laura Silvana Castillo,Marcel Catà,Philippe C. Cattin,Albert Cerigues,Vinicius S. Chagas,Siddhartha Chandra,Yi-Ju Chang,Shiyu Chang,Ken Chang,Joseph Chazalon,Shengcong Chen,Wei Chen,Jefferson W. Chen,Zhaolin Chen,Kun Cheng,Ahana Roy Choudhury,Roger Chylla,Albert Clèrigues,Steven Colleman,Ramiro German Rodriguez Colmeiro,Ramiro German Rodriguez Colmeiro,Marc Combalia,Anthony Costa,Xiaomeng Cui,Zhenzhen Dai,Lutao Dai,Laura Alexandra Daza,Eric Deutsch,Changxing Ding,Chao Dong,Shidu Dong,Wojciech Dudzik,Zach Eaton-Rosen,Gary F. Egan,Guilherme Escudero,Théo Estienne,Théo Estienne,Richard M. Everson,Jonathan Fabrizio,Yong Fan,Longwei Fang,Xue Feng,Enzo Ferrante,Lucas Fidon,Martin H. Fischer,Andrew P. French,Naomi Fridman,Huan Fu,David Fuentes,Yaozong Gao,Evan Gates,David T. Gering,Amir Gholami,Willi Gierke,Ben Glocker,Mingming Gong,Mingming Gong,Sandra González-Villà,Thomas Grosges,Yuanfang Guan,Sheng Guo,Sudeep Gupta,Woo-Sup Han,Il Song Han,Konstantin Harmuth,Huiguang He,Aura Hernández-Sabaté,Evelyn Herrmann,Naveen Himthani,Winston H. Hsu,Cheyu Hsu,Hu Xiaojun,Xiaobin Hu,Yan Hu,Yifan Hu,Rui Hua,Teng-Yi Huang,Weilin Huang,Sabine Van Huffel,Quan Huo,Vivek Hv,Khan M. Iftekharuddin,Fabian Isensee,Mobarakol Islam,Aaron S. Jackson,Sachin Jambawalikar,Andrew Jesson,Weijian Jian,Peter H. Jin,V Jeya Maria Jose,V Jeya Maria Jose,Alain Jungo,Bernhard Kainz,Konstantinos Kamnitsas,Po-Yu Kao,Ayush Karnawat,Thomas Kellermeier,Adel Kermi,Kurt Keutzer,Mohamed Tarek Khadir,Mahendra Khened,Philipp Kickingereder,Geena Kim,Nik King,Haley Knapp,Urspeter Knecht,Lisa Kohli,Deren Kong,Xiangmao Kong,Simon Koppers,Avinash Kori,Ganapathy Krishnamurthi,Egor Krivov,Piyush Kumar,Kaisar Kushibar,Dmitrii Lachinov,Dmitrii Lachinov,Tryphon Lambrou,Joon Lee,Chengen Lee,Yuehchou Lee,Matthew C. H. Lee,Szidónia Lefkovits,László Lefkovits,James Levitt,Tengfei Li,Hongwei Li,Wenqi Li,Wenqi Li,Hongyang Li,Xiaochuan Li,Yuexiang Li,Heng Li,Zhenye Li,Xiaoyu Li,Zeju Li,XiaoGang Li,Zheng-Shen Lin,Fengming Lin,Pietro Liò,C Liu,Boqiang Liu,Xiang Liu,Mingyuan Liu,Ju Liu,Luyan Liu,Xavier Lladó,Marc Moreno Lopez,Pablo Ribalta Lorenzo,Zhentai Lu,Lin Luo,Zhigang Luo,Jun Ma,Kai Ma,Thomas Mackie,Anant Madabushi,Issam Mahmoudi,Klaus H. Maier-Hein,Pradipta Maji,C. P. Mammen,Andreas Mang,B.S. Manjunath,M. Marcinkiewicz,Steven McDonagh,Stephen J. McKenna,Richard McKinley,Miriam Mehl,Sachin Mehta,Raghav Mehta,Raphael Meier,Christoph Meinel,Dorit Merhof,Craig H. Meyer,Robert F. Miller,Sushmita Mitra,Aliasgar Moiyadi,David Molina-García,Miguel Monteiro,Grzegorz Mrukwa,Andriy Myronenko,Jakub Nalepa,Thuyen Ngo,Dong Nie,Holly Ning,Chen Niu,Nicholas Nuechterlein,Eric K. Oermann,Arlindo L. Oliveira,Arlindo L. Oliveira,Diego D. C. Oliveira,Arnau Oliver,Alexander F. I. Osman,Yu-Nian Ou,Sebastien Ourselin,Nikos Paragios,Moo Sung Park,Brad Paschke,J. Gregory Pauloski,Kamlesh Pawar,Nick Pawlowski,Linmin Pei,Suting Peng,Silvio M. Pereira,Julián Pérez-Beteta,Víctor M. Pérez-García,Simon Pezold,Bao Pham,Ashish Phophalia,Gemma Piella,G. N. Pillai,Marie Piraud,Maxim Pisov,Anmol Popli,Michael P. Pound,Reza Pourreza,Prateek Prasanna,Vesna Prkovska,Tony P. Pridmore,Santi Puch,Elodie Puybareau,Buyue Qian,Xu Qiao,Martin Rajchl,Swapnil Rane,Michael Rebsamen,Hongliang Ren,Xuhua Ren,Karthik Revanuru,Mina Rezaei,Oliver Rippel,Luis Carlos Rivera,Charlotte Robert,Bruce R. Rosen,Daniel Rueckert,Mohammed Safwan,Mostafa Salem,Joaquim Salvi,Irina Sánchez,Heitor M. Santos,Emmett Sartor,Dawid Schellingerhout,Klaudius Scheufele,Matthew R. Scott,Artur A. Scussel,Sara Sedlar,Juan Pablo Serrano-Rubio,N. Jon Shah,Nameetha Shah,Mazhar Shaikh,B. Uma Shankar,Zeina A. Shboul,Haipeng Shen,Dinggang Shen,Linlin Shen,Haocheng Shen,Varun Shenoy,Feng Shi,Hyung Eun Shin,Hai Shu,Diana M. Sima,Matthew Sinclair,Örjan Smedby,James Snyder,Mohammadreza Soltaninejad,Guidong Song,Mehul Soni,Jean Stawiaski,Shashank Subramanian,Li Sun,Roger Sun,Roger Sun,Jiawei Sun,Kay Sun,Yu Sun,Guoxia Sun,Shuang Sun,Yannick Suter,László Szilágyi,Sanjay N. Talbar,Dacheng Tao,Zhongzhao Teng,Siddhesh Thakur,Meenakshi Thakur,Sameer Tharakan,Pallavi Tiwari,Guillaume Tochon,Tuan Tran,Yuhsiang M. Tsai,Kuan-Lun Tseng,Tran Anh Tuan,Vadim Turlapov,Nicholas J. Tustison,Maria Vakalopoulou,Sergi Valverde,Rami Vanguri,Evgeny Vasiliev,Jonathan Ventura,Luis Vera,Tom Vercauteren,Tom Vercauteren,C. A. Verrastro,Lasitha Vidyaratne,Verónica Vilaplana,Ajeet Vivekanandan,Guotai Wang,Guotai Wang,Qian Wang,Chiatse J. Wang,Weichung Wang,Duo Wang,Ruixuan Wang,Yuanyuan Wang,Chunliang Wang,Ning Wen,Xin Wen,Leon Weninger,Wolfgang Wick,Shaocheng Wu,Qiang Wu,Yihong Wu,Yong Xia,Yanwu Xu,Xiaowen Xu,Peiyuan Xu,Tsai-Ling Yang,Xiaoping Yang,Hao-Yu Yang,Junlin Yang,Haojin Yang,Guang Yang,Hongdou Yao,Xujiong Ye,Changchang Yin,Brett Young-Moxon,Jinhua Yu,Xiangyu Yue,Songtao Zhang,Angela Zhang,Kun Zhang,Xuejie Zhang,Lichi Zhang,Xiaoyue Zhang,Yazhuo Zhang,Lei Zhang,Jianguo Zhang,Xiang Zhang,Tianhao Zhang,Sicheng Zhao,Yu Zhao,Xiaomei Zhao,Liang Zhao,Liang Zhao,Yefeng Zheng,Liming Zhong,Chenhong Zhou,Xiaobing Zhou,Fan Zhou,Hongtu Zhu,Jin Zhu,Ying Zhuge,Weiwei Zong,Jayashree Kalpathy-Cramer,Keyvan Farahani,Christos Davatzikos,Koen Van Leemput,Koen Van Leemput,Bjoern H. Menze +438 more
TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.
Journal ArticleDOI
Cross-Modality Deep Feature Learning for Brain Tumor Segmentation
TL;DR: The proposed cross-modality deep feature learning framework can effectively improve the brain tumor segmentation performance when compared with the baseline methods and state-of-the-art methods.
Journal ArticleDOI
A survey on U-shaped networks in medical image segmentations
Liangliang Liu,Liangliang Liu,Jianhong Cheng,Quan Quan,Fang-Xiang Wu,Yu-Ping Wang,Jianxin Wang +6 more
TL;DR: A comprehensive literature review of U-shaped networks applied to medical image segmentation tasks, focusing on the architectures, extended mechanisms and application areas in these studies.
Journal ArticleDOI
Attention Gate ResU-Net for Automatic MRI Brain Tumor Segmentation
TL;DR: Experimental results illuminate that models with attention gate units, i.e., Attention Gate U-Net (AGU-Net) and AGResU- Net, outperform their baselines of U- net and ResU- net, respectively and achieves competitive performance than the representative brain tumor segmentation methods.
Journal ArticleDOI
Exploring Task Structure for Brain Tumor Segmentation From Multi-Modality MR Images
TL;DR: The proposed novel task-structured brain tumor segmentation network (TSBTS net) achieves superior performance in segmenting the desired brain tumor areas while requiring relatively lower computational costs, compared to other state-of-the-art methods and baseline models.
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