Overview of convolutional neural networks architectures for brain tumor segmentation

Due to the paramount importance of the medical field in the lives of people, researchers and experts exploited advancements in computer techniques to solve many diagnostic and analytical medical problems. Brain tumor diagnosis is one of the most important computational problems that has been studied and focused on. The brain tumor is determined by segmentation of brain images using many techniques based on magnetic resonance imaging (MRI). Brain tumor segmentation methods have been developed since a long time and are still evolving, but the current trend is to use deep convolutional neural networks (CNNs) due to its many breakthroughs and unprecedented results that have been achieved in various applications and their capacity to learn a hierarchy of progressively complicated characteristics from input without requiring manual feature extraction. Considering these unprecedented results, we present this paper as a brief review for main CNNs architecture types used in brain tumor segmentation. Specifically, we focus on researcher works that used the well-known brain tumor segmentation (BraTS) dataset

U-Net and its variants for medical image segmentation: theory and applications

U-net is an image segmentation technique developed primarily for medical image analysis that can precisely segment images using a scarce amount of training data. These traits provide U-net with a very high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in all major image modalities from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. As the potential of U-net is still increasing, in this review we look at the various developments that have been made in the U-net architecture and provide observations on recent trends. We examine the various innovations that have been made in deep learning and discuss how these tools facilitate U-net. Furthermore, we look at image modalities and application areas where U-net has been applied.Comment: 42 pages, in IEEE Acces

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

International Brain Tumor Segmentation (BraTS) challengeGliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) 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. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.This work was supported in part by the 1) National Institute of Neurological Disorders and Stroke (NINDS) of the NIH R01 grant with award number R01-NS042645, 2) Informatics Technology for Cancer Research (ITCR) program of the NCI/NIH U24 grant with award number U24-CA189523, 3) Swiss Cancer League, under award number KFS-3979-08-2016, 4) Swiss National Science Foundation, under award number 169607.Article signat per 427 autors/es: Spyridon Bakas1,2,3,†,‡,∗ , Mauricio Reyes4,† , Andras Jakab5,†,‡ , Stefan Bauer4,6,169,† , Markus Rempfler9,65,127,† , Alessandro Crimi7,† , Russell Takeshi Shinohara1,8,† , Christoph Berger9,† , Sung Min Ha1,2,† , Martin Rozycki1,2,† , Marcel Prastawa10,† , Esther Alberts9,65,127,† , Jana Lipkova9,65,127,† , John Freymann11,12,‡ , Justin Kirby11,12,‡ , Michel Bilello1,2,‡ , Hassan M. Fathallah-Shaykh13,‡ , Roland Wiest4,6,‡ , Jan Kirschke126,‡ , Benedikt Wiestler126,‡ , Rivka Colen14,‡ , Aikaterini Kotrotsou14,‡ , Pamela Lamontagne15,‡ , Daniel Marcus16,17,‡ , Mikhail Milchenko16,17,‡ , Arash Nazeri17,‡ , Marc-Andr Weber18,‡ , Abhishek Mahajan19,‡ , Ujjwal Baid20,‡ , Elizabeth Gerstner123,124,‡ , Dongjin Kwon1,2,† , Gagan Acharya107, Manu Agarwal109, Mahbubul Alam33 , Alberto Albiol34, Antonio Albiol34, Francisco J. Albiol35, Varghese Alex107, Nigel Allinson143, Pedro H. A. Amorim159, Abhijit Amrutkar107, Ganesh Anand107, Simon Andermatt152, Tal Arbel92, Pablo Arbelaez134, Aaron Avery60, Muneeza Azmat62, Pranjal B.107, Wenjia Bai128, Subhashis Banerjee36,37, Bill Barth2 , Thomas Batchelder33, Kayhan Batmanghelich88, Enzo Battistella42,43 , Andrew Beers123,124, Mikhail Belyaev137, Martin Bendszus23, Eze Benson38, Jose Bernal40 , Halandur Nagaraja Bharath141, George Biros62, Sotirios Bisdas76, James Brown123,124, Mariano Cabezas40, Shilei Cao67, Jorge M. Cardoso76, Eric N Carver41, Adri Casamitjana138, Laura Silvana Castillo134, Marcel Cat138, Philippe Cattin152, Albert Cerigues ´ 40, Vinicius S. Chagas159 , Siddhartha Chandra42, Yi-Ju Chang45, Shiyu Chang156, Ken Chang123,124, Joseph Chazalon29 , Shengcong Chen25, Wei Chen46, Jefferson W Chen80, Zhaolin Chen130, Kun Cheng120, Ahana Roy Choudhury47, Roger Chylla60, Albert Clrigues40, Steven Colleman141, Ramiro German Rodriguez Colmeiro149,150,151, Marc Combalia138, Anthony Costa122, Xiaomeng Cui115, Zhenzhen Dai41, Lutao Dai50, Laura Alexandra Daza134, Eric Deutsch43, Changxing Ding25, Chao Dong65 , Shidu Dong155, Wojciech Dudzik71,72, Zach Eaton-Rosen76, Gary Egan130, Guilherme Escudero159, Tho Estienne42,43, Richard Everson87, Jonathan Fabrizio29, Yong Fan1,2 , Longwei Fang54,55, Xue Feng27, Enzo Ferrante128, Lucas Fidon42, Martin Fischer95, Andrew P. French38,39 , Naomi Fridman57, Huan Fu90, David Fuentes58, Yaozong Gao68, Evan Gates58, David Gering60 , Amir Gholami61, Willi Gierke95, Ben Glocker128, Mingming Gong88,89, Sandra Gonzlez-Vill40, T. Grosges151, Yuanfang Guan108, Sheng Guo64, Sudeep Gupta19, Woo-Sup Han63, Il Song Han63 , Konstantin Harmuth95, Huiguang He54,55,56, Aura Hernndez-Sabat100, Evelyn Herrmann102 , Naveen Himthani62, Winston Hsu111, Cheyu Hsu111, Xiaojun Hu64, Xiaobin Hu65, Yan Hu66, Yifan Hu117, Rui Hua68,69, Teng-Yi Huang45, Weilin Huang64, Sabine Van Huffel141, Quan Huo68, Vivek HV70, Khan M. Iftekharuddin33, Fabian Isensee22, Mobarakol Islam81,82, Aaron S. Jackson38 , Sachin R. Jambawalikar48, Andrew Jesson92, Weijian Jian119, Peter Jin61, V Jeya Maria Jose82,83 , Alain Jungo4 , Bernhard Kainz128, Konstantinos Kamnitsas128, Po-Yu Kao79, Ayush Karnawat129 , Thomas Kellermeier95, Adel Kermi74, Kurt Keutzer61, Mohamed Tarek Khadir75, Mahendra Khened107, Philipp Kickingereder23, Geena Kim135, Nik King60, Haley Knapp60, Urspeter Knecht4 , Lisa Kohli60, Deren Kong64, Xiangmao Kong115, Simon Koppers32, Avinash Kori107, Ganapathy Krishnamurthi107, Egor Krivov137, Piyush Kumar47, Kaisar Kushibar40, Dmitrii Lachinov84,85 , Tryphon Lambrou143, Joon Lee41, Chengen Lee111, Yuehchou Lee111, Matthew Chung Hai Lee128 , Szidonia Lefkovits96, Laszlo Lefkovits97, James Levitt62, Tengfei Li51, Hongwei Li65, Wenqi Li76,77 , Hongyang Li108, Xiaochuan Li110, Yuexiang Li133, Heng Li51, Zhenye Li146, Xiaoyu Li67, Zeju Li158 , XiaoGang Li162, Wenqi Li76,77, Zheng-Shen Lin45, Fengming Lin115, Pietro Lio153, Chang Liu41 , Boqiang Liu46, Xiang Liu67, Mingyuan Liu114, Ju Liu115,116, Luyan Liu112, Xavier Llado´ 40, Marc Moreno Lopez132, Pablo Ribalta Lorenzo72, Zhentai Lu53, Lin Luo31, Zhigang Luo162, Jun Ma73 , Kai Ma117, Thomas Mackie60, Anant Madabhushi129, Issam Mahmoudi74, Klaus H. Maier-Hein22 , Pradipta Maji36, CP Mammen161, Andreas Mang165, B. S. Manjunath79, Michal Marcinkiewicz71 , Steven McDonagh128, Stephen McKenna157, Richard McKinley6 , Miriam Mehl166, Sachin Mehta91 , Raghav Mehta92, Raphael Meier4,6 , Christoph Meinel95, Dorit Merhof32, Craig Meyer27,28, Robert Miller131, Sushmita Mitra36, Aliasgar Moiyadi19, David Molina-Garcia142, Miguel A.B. Monteiro105 , Grzegorz Mrukwa71,72, Andriy Myronenko21, Jakub Nalepa71,72, Thuyen Ngo79, Dong Nie113, Holly Ning131, Chen Niu67, Nicholas K Nuechterlein91, Eric Oermann122, Arlindo Oliveira105,106, Diego D. C. Oliveira159, Arnau Oliver40, Alexander F. I. Osman140, Yu-Nian Ou45, Sebastien Ourselin76 , Nikos Paragios42,44, Moo Sung Park121, Brad Paschke60, J. Gregory Pauloski58, Kamlesh Pawar130, Nick Pawlowski128, Linmin Pei33, Suting Peng46, Silvio M. Pereira159, Julian Perez-Beteta142, Victor M. Perez-Garcia142, Simon Pezold152, Bao Pham104, Ashish Phophalia136 , Gemma Piella101, G.N. Pillai109, Marie Piraud65, Maxim Pisov137, Anmol Popli109, Michael P. Pound38, Reza Pourreza131, Prateek Prasanna129, Vesna Pr?kovska99, Tony P. Pridmore38, Santi Puch99, lodie Puybareau29, Buyue Qian67, Xu Qiao46, Martin Rajchl128, Swapnil Rane19, Michael Rebsamen4 , Hongliang Ren82, Xuhua Ren112, Karthik Revanuru139, Mina Rezaei95, Oliver Rippel32, Luis Carlos Rivera134, Charlotte Robert43, Bruce Rosen123,124, Daniel Rueckert128 , Mohammed Safwan107, Mostafa Salem40, Joaquim Salvi40, Irina Sanchez138, Irina Snchez99 , Heitor M. Santos159, Emmett Sartor160, Dawid Schellingerhout59, Klaudius Scheufele166, Matthew R. Scott64, Artur A. Scussel159, Sara Sedlar139, Juan Pablo Serrano-Rubio86, N. Jon Shah130 , Nameetha Shah139, Mazhar Shaikh107, B. Uma Shankar36, Zeina Shboul33, Haipeng Shen50 , Dinggang Shen113, Linlin Shen133, Haocheng Shen157, Varun Shenoy61, Feng Shi68, Hyung Eun Shin121, Hai Shu52, Diana Sima141, Matthew Sinclair128, Orjan Smedby167, James M. Snyder41 , Mohammadreza Soltaninejad143, Guidong Song145, Mehul Soni107, Jean Stawiaski78, Shashank Subramanian62, Li Sun30, Roger Sun42,43, Jiawei Sun46, Kay Sun60, Yu Sun69, Guoxia Sun115 , Shuang Sun115, Yannick R Suter4 , Laszlo Szilagyi97, Sanjay Talbar20, Dacheng Tao26, Dacheng Tao90, Zhongzhao Teng154, Siddhesh Thakur20, Meenakshi H Thakur19, Sameer Tharakan62 , Pallavi Tiwari129, Guillaume Tochon29, Tuan Tran103, Yuhsiang M. Tsai111, Kuan-Lun Tseng111 , Tran Anh Tuan103, Vadim Turlapov85, Nicholas Tustison28, Maria Vakalopoulou42,43, Sergi Valverde40, Rami Vanguri48,49, Evgeny Vasiliev85, Jonathan Ventura132, Luis Vera142, Tom Vercauteren76,77, C. A. Verrastro149,150, Lasitha Vidyaratne33, Veronica Vilaplana138, Ajeet Vivekanandan60, Guotai Wang76,77, Qian Wang112, Chiatse J. Wang111, Weichung Wang111, Duo Wang153, Ruixuan Wang157, Yuanyuan Wang158, Chunliang Wang167, Guotai Wang76,77, Ning Wen41, Xin Wen67, Leon Weninger32, Wolfgang Wick24, Shaocheng Wu108, Qiang Wu115,116 , Yihong Wu144, Yong Xia66, Yanwu Xu88, Xiaowen Xu115, Peiyuan Xu117, Tsai-Ling Yang45 , Xiaoping Yang73, Hao-Yu Yang93,94, Junlin Yang93, Haojin Yang95, Guang Yang170, Hongdou Yao98, Xujiong Ye143, Changchang Yin67, Brett Young-Moxon60, Jinhua Yu158, Xiangyu Yue61 , Songtao Zhang30, Angela Zhang79, Kun Zhang89, Xuejie Zhang98, Lichi Zhang112, Xiaoyue Zhang118, Yazhuo Zhang145,146,147, Lei Zhang143, Jianguo Zhang157, Xiang Zhang162, Tianhao Zhang168, Sicheng Zhao61, Yu Zhao65, Xiaomei Zhao144,55, Liang Zhao163,164, Yefeng Zheng117 , Liming Zhong53, Chenhong Zhou25, Xiaobing Zhou98, Fan Zhou51, Hongtu Zhu51, Jin Zhu153, Ying Zhuge131, Weiwei Zong41, Jayashree Kalpathy-Cramer123,124,† , Keyvan Farahani12,†,‡ , Christos Davatzikos1,2,†,‡ , Koen van Leemput123,124,125,† , and Bjoern Menze9,65,127,†,∗Preprin

3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context

We present an efficient deep learning approach for the challenging task of tumor segmentation in multisequence MR images. In recent years, Convolutional Neural Networks (CNN) have achieved state-of-the-art performances in a large variety of recognition tasks in medical imaging. Because of the considerable computational cost of CNNs, large volumes such as MRI are typically processed by subvolumes, for instance slices (axial, coronal, sagittal) or small 3D patches. In this paper we introduce a CNN-based model which efficiently combines the advantages of the short-range 3D context and the long-range 2D context. To overcome the limitations of specific choices of neural network architectures, we also propose to merge outputs of several cascaded 2D-3D models by a voxelwise voting strategy. Furthermore, we propose a network architecture in which the different MR sequences are processed by separate subnetworks in order to be more robust to the problem of missing MR sequences. Finally, a simple and efficient algorithm for training large CNN models is introduced. We evaluate our method on the public benchmark of the BRATS 2017 challenge on the task of multiclass segmentation of malignant brain tumors. Our method achieves good performances and produces accurate segmentations with median Dice scores of 0.918 (whole tumor), 0.883 (tumor core) and 0.854 (enhancing core). Our approach can be naturally applied to various tasks involving segmentation of lesions or organs.Comment: Submitted to the journal Computerized Medical Imaging and Graphic

Magnetic resonance image-based brain tumour segmentation methods : a systematic review

Background: Image segmentation is an essential step in the analysis and subsequent characterisation of brain tumours through magnetic resonance imaging. In the literature, segmentation methods are empowered by open-access magnetic resonance imaging datasets, such as the brain tumour segmentation dataset. Moreover, with the increased use of artificial intelligence methods in medical imaging, access to larger data repositories has become vital in method development. Purpose: To determine what automated brain tumour segmentation techniques can medical imaging specialists and clinicians use to identify tumour components, compared to manual segmentation. Methods: We conducted a systematic review of 572 brain tumour segmentation studies during 2015–2020. We reviewed segmentation techniques using T1-weighted, T2-weighted, gadolinium-enhanced T1-weighted, fluid-attenuated inversion recovery, diffusion-weighted and perfusion-weighted magnetic resonance imaging sequences. Moreover, we assessed physics or mathematics-based methods, deep learning methods, and software-based or semi-automatic methods, as applied to magnetic resonance imaging techniques. Particularly, we synthesised each method as per the utilised magnetic resonance imaging sequences, study population, technical approach (such as deep learning) and performance score measures (such as Dice score). Statistical tests: We compared median Dice score in segmenting the whole tumour, tumour core and enhanced tumour. Results: We found that T1-weighted, gadolinium-enhanced T1-weighted, T2-weighted and fluid-attenuated inversion recovery magnetic resonance imaging are used the most in various segmentation algorithms. However, there is limited use of perfusion-weighted and diffusion-weighted magnetic resonance imaging. Moreover, we found that the U-Net deep learning technology is cited the most, and has high accuracy (Dice score 0.9) for magnetic resonance imaging-based brain tumour segmentation. Conclusion: U-Net is a promising deep learning technology for magnetic resonance imaging-based brain tumour segmentation. The community should be encouraged to contribute open-access datasets so training, testing and validation of deep learning algorithms can be improved, particularly for diffusion- and perfusion-weighted magnetic resonance imaging, where there are limited datasets available

A Review on Brain Tumor Segmentation Based on Deep Learning Methods with Federated Learning Techniques

Brain tumors have become a severe medical complication in recent years due to their high fatality rate. Radiologists segment the tumor manually, which is time-consuming, error-prone, and expensive. In recent years, automated segmentation based on deep learning has demonstrated promising results in solving computer vision problems such as image classification and segmentation. Brain tumor segmentation has recently become a prevalent task in medical imaging to determine the tumor location, size, and shape using automated methods. Many researchers have worked on various machine and deep learning approaches to determine the most optimal solution using the convolutional methodology. In this review paper, we discuss the most effective segmentation techniques based on the datasets that are widely used and publicly available. We also proposed a survey of federated learning methodologies to enhance global segmentation performance and ensure privacy. A comprehensive literature review is suggested after studying more than 100 papers to generalize the most recent techniques in segmentation and multi-modality information. Finally, we concentrated on unsolved problems in brain tumor segmentation and a client-based federated model training strategy. Based on this review, future researchers will understand the optimal solution path to solve these issues

Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information.

The manual brain tumor annotation process is time consuming and resource consuming, therefore, an automated and accurate brain tumor segmentation tool is greatly in demand. In this paper, we introduce a novel method to integrate location information with the state-of-the-art patch-based neural networks for brain tumor segmentation. This is motivated by the observation that lesions are not uniformly distributed across different brain parcellation regions and that a locality-sensitive segmentation is likely to obtain better segmentation accuracy. Toward this, we use an existing brain parcellation atlas in the Montreal Neurological Institute (MNI) space and map this atlas to the individual subject data. This mapped atlas in the subject data space is integrated with structural Magnetic Resonance (MR) imaging data, and patch-based neural networks, including 3D U-Net and DeepMedic, are trained to classify the different brain lesions. Multiple state-of-the-art neural networks are trained and integrated with XGBoost fusion in the proposed two-level ensemble method. The first level reduces the uncertainty of the same type of models with different seed initializations, and the second level leverages the advantages of different types of neural network models. The proposed location information fusion method improves the segmentation performance of state-of-the-art networks including 3D U-Net and DeepMedic. Our proposed ensemble also achieves better segmentation performance compared to the state-of-the-art networks in BraTS 2017 and rivals state-of-the-art networks in BraTS 2018. Detailed results are provided on the public multimodal brain tumor segmentation (BraTS) benchmarks

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201