Brain tumor segmentation in multi‐spectral MRI using convolutional neural networks (CNN)

Iqbal Sajid; Ghani M. Usman; Saba Tanzila; Rehman Amjad; Saggau Peter

摘要

Abstract >A tumor could be found in any area of the brain and could be of any size, shape, and contrast. There may exist multiple tumors of different types in a human brain at the same time. Accurate tumor area segmentation is considered primary step for treatment of brain tumors. Deep Learning is a set of promising techniques that could provide better results as compared to nondeep learning techniques for segmenting timorous part inside a brain. This article presents a deep convolutional neural network (CNN) to segment brain tumors in MRIs. The proposed network uses BRATS segmentation challenge dataset which is composed of images obtained through four different modalities. Accordingly, we present an extended version of existing network to solve segmentation problem. The network architecture consists of multiple neural network layers connected in sequential order with the feeding of Convolutional feature maps at the peer level. Experimental results on BRATS 2015 benchmark data thus show the usability of the proposed approach and its superiority over the other approaches in this area of research. </abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> <div class="translation abstracttxt"> <span class="zhankaihshouqi fivelineshidden" id="abstract"> <span>机译：</span><abstract xmlns =“http://www.wiley.com/namespaces/wiley”type =“main”> <title type =“main”>抽象</ title> >肿瘤可以在任何区域找到大脑并且可能具有任何尺寸，形状和对比度。同时可能存在人类大脑中不同类型的多种肿瘤。准确的肿瘤区域分割被认为是治疗脑肿瘤的主要步骤。深入学习是一系列希望的技术可以提供与用于在大脑内部分割的胆小组器部分分割的Nondeep学习技术相比提供更好的结果。本文呈现了深度卷积神经网络（CNN），用于在MRIS中分段脑肿瘤。所提出的网络使用BRATS分段挑战数据集，其由通过四种不同方式获得的图像组成。因此，我们呈现了一个扩展版本的现有网络来解决分割问题。网络架构由以顺序连接的多个神经网络层组成，在对等级别的卷积特征映射的馈送。 Brats 2015基准数据的实验结果表明，拟议方法的可用性及其在该研究领域其他方法的优势。</ p> </摘要> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> </div> <div class="record"> <h2 class="all_title" id="enpatent33" >著录项</h2> <ul> <li> <span class="lefttit">来源</span> <div style="width: 86%;vertical-align: text-top;display: inline-block;"> <a href='/journal-foreign-24994/'>《Microscopy research and technique》</a> <b style="margin: 0 2px;">|</b><span>2018年第4期</span><b style="margin: 0 2px;">|</b><span>共9页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Iqbal Sajid&option=202" target="_blank" rel="nofollow">Iqbal Sajid;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Ghani M. Usman&option=202" target="_blank" rel="nofollow">Ghani M. Usman;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Saba Tanzila&option=202" target="_blank" rel="nofollow">Saba Tanzila;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Rehman Amjad&option=202" target="_blank" rel="nofollow">Rehman Amjad;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Saggau Peter&option=202" target="_blank" rel="nofollow">Saggau Peter;</a> </p> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zkzz" style="display: none;">展开▼</span> </div> </li> <li> <div style="display: flex;"> <span class="lefttit">作者单位</span> <div style="position: relative;margin-left: 3px;max-width: 639px;"> <div class="threelineshidden zhankaihshouqi" id="fOrgthree"> <p>Department of Computer Science and EngineeringUniversity of Engineering and TechnologyLahore Pakistan;</p> <p>Department of Computer Science and EngineeringUniversity of Engineering and TechnologyLahore Pakistan;</p> <p>College of Computer and Information SciencesPrince Sultan UniversityRiyadh 11586 Saudi Arabia;</p> <p>College of Computer and Information SystemsAl Yamamah UniversityRiyadh 11512 Saudi Arabia;</p> </div> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zhdw" style="display: none;">展开▼</span> </div> </div> </li> <li > <span class="lefttit">收录信息</span> <span style="width: 86%;vertical-align: text-top;display: inline-block;"></span> </li> <li> <span class="lefttit">原文格式</span> <span>PDF</span> </li> <li> <span class="lefttit">正文语种</span> <span>eng</span> </li> <li> <span class="lefttit">中图分类</span> <span><a href="https://www.zhangqiaokeyan.com/clc/19294.html" title="显微镜">显微镜;</a></span> </li> <li class="antistop"> <span class="lefttit">关键词</span> <p style="width: 86%;vertical-align: text-top;"> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=BRATS datasets&option=203" rel="nofollow">BRATS datasets;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=convolutional neural networks&option=203" rel="nofollow">convolutional neural networks;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=deep learning&option=203" rel="nofollow">deep learning;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=features mining&option=203" rel="nofollow">features mining;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=tumor segmentation&option=203" rel="nofollow">tumor segmentation;</a> </p> <div class="translation"> 机译：BRATS数据集;卷积神经网络;深入学习;特征挖掘;肿瘤细分; </div> </li> </ul> </div> </div> <div class="literature cardcommon"> <div class="similarity "> <h3 class="all_title" id="enpatent66">相似文献</h3> <div class="similaritytab clearfix"> <ul> <li class="active" >外文文献</li> <li >中文文献</li> <li >专利</li> </ul> </div> <div class="similarity_details"> <ul > <li> <div> <b>1. </b><a class="enjiyixqcontent" href="/journal-foreign-detail/0704023680139.html">Brain tumor segmentation in multi‐spectral MRI using convolutional neural networks (CNN)</a> <b>[J]</b> . <span> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Iqbal Sajid&option=202" target="_blank" rel="nofollow" class="tuijian_auth tuijian_authcolor">Iqbal Sajid,</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Ghani M. 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