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Dissertations / Theses on the topic 'Brain tumor segmentation dataset'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

Havaei, Seyed Mohammad. "Machine learning methods for brain tumor segmentation." Thèse, Université de Sherbrooke, 2017. http://hdl.handle.net/11143/10260.

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Abstract : Malignant brain tumors are the second leading cause of cancer related deaths in children under 20. There are nearly 700,000 people in the U.S. living with a brain tumor and 17,000 people are likely to loose their lives due to primary malignant and central nervous system brain tumor every year. To identify whether a patient is diagnosed with brain tumor in a non-invasive way, an MRI scan of the brain is acquired followed by a manual examination of the scan by an expert who looks for lesions (i.e. cluster of cells which deviate from healthy tissue). For treatment purposes, the tumor

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Gering, David T. (David Thomas) 1971. "Recognizing deviations from normalcy for brain tumor segmentation." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/28275.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.<br>Includes bibliographical references (p. 180-189).<br>A framework is proposed for the segmentation of brain tumors from MRI. Instead of training on pathology, the proposed method trains exclusively on healthy tissue. The algorithm attempts to recognize deviations from normalcy in order to compute a fitness map over the image associated with the presence of pathology. The resulting fitness map may then be used by conventional image segmentation techniques for honing in on bounda

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Lau, Kiu Wai. "Representation Learning on Brain MR Images for Tumor Segmentation." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234827.

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MRI is favorable for brain imaging due to its excellent soft tissue contrast and absence of harmful ionizing radiation. Many have proposed supervised multimodal neural networks for automatic brain tumor segmentation and showed promising results. However, they rely on large amounts of labeled data to generalize well. The trained network is also highly specific to the task and input. Missing inputs will most likely have a detrimental effect on the network’s predictions, if it works at all. The aim of this thesis work is to implement a deep neural network that learns the general representation of

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Balsiger, Fabian. "Brain Tumor Volume Calculation : Segmentation and Visualization Using MR Images." Thesis, Linköpings universitet, Biomedicinsk instrumentteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-80351.

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Background: Glioblastomas are highly aggressive and malignant brain tumors which are difficult to resect totally. The surgical extent of resection constitutes a key role due to its direct influence on the patient’s survival time. To determine the resection extent, the tumor volume on pre-operative and post-operative magnetic resonance (MR) images should be calculated and compared. Materials and Methods: An active contour segmentation method was implemented to segment glioblastoma brain tumors on pre-operative T1-contrast enhanced MR images in axial, coronal and sagittal planes by self-develope

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Westermark, Hanna. "Deep Learning with Importance Sampling for Brain Tumor MR Segmentation." Thesis, KTH, Optimeringslära och systemteori, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289574.

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Segmentation of magnetic resonance images is an important part of planning radiotherapy treat-ments for patients with brain tumours but due to the number of images contained within a scan and the level of detail required, manual segmentation is a time consuming task. Convolutional neural networks have been proposed as tools for automated segmentation and shown promising results. However, the data sets used for training these deep learning models are often imbalanced and contain data that does not contribute to the performance of the model. By carefully selecting which data to train on, there i

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Li, Xiaobing. "Automatic image segmentation based on level set approach: application to brain tumor segmentation in MR images." Reims, 2009. http://theses.univ-reims.fr/exl-doc/GED00001120.pdf.

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L'objectif de la thèse est de développer une segmentation automatique des tumeurs cérébrales à partir de volumes IRM basée sur la technique des « level sets ». Le fonctionnement «automatique» de ce système utilise le fait que le cerveau normal est symétrique et donc la localisation des régions dissymétriques permet d'estimer le contour initial de la tumeur. La première étape concerne le prétraitement qui consiste à corriger l'inhomogénéité de l'intensité du volume IRM et à recaler spatialement les volumes d'IRM d'un même patient à différents instants. Le plan hémisphérique du cerveau est reche

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7

Zhang, Nan. "Feature selection based segmentation of multi-source images : application to brain tumor segmentation in multi-sequence MRI." Phd thesis, INSA de Lyon, 2011. http://tel.archives-ouvertes.fr/tel-00701545.

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Multi-spectral images have the advantage of providing complementary information to resolve some ambiguities. But, the challenge is how to make use of the multi-spectral images effectively. In this thesis, our study focuses on the fusion of multi-spectral images by extracting the most useful features to obtain the best segmentation with the least cost in time. The Support Vector Machine (SVM) classification integrated with a selection of the features in a kernel space is proposed. The selection criterion is defined by the kernel class separability. Based on this SVM classification, a framework

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Gordillo, Castillo Nelly. "Contributions to Automatic and Unsupervised MRI Brain Tumor Segmentation: A New Fuzzy Approach." Doctoral thesis, Universitat Politècnica de Catalunya, 2010. http://hdl.handle.net/10803/6210.

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Brain tumors are part of a group of common, non-communicable, chronic and potentially lethal diseases affecting mostfamilies in Europe. Imaging plays a central role in brain tumor management, from detection and classification to staging andcomparison. <br/>Increasingly, magnetic resonance imaging (MRI) scan is being used for suspected brain tumors, because in addition tooutline the normal brain structures in great detail, has a high sensitivity for detecting the presence of, or changes within, a tumor.Currently most of the process related to brain tumors such as diagnosis, therapy, and surgery

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Geremia, Ezequiel. "Spatial random forests for brain lesions segmentation in MRIs and model-based tumor cell extrapolation." Phd thesis, Université Nice Sophia Antipolis, 2013. http://tel.archives-ouvertes.fr/tel-00838795.

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The large size of the datasets produced by medical imaging protocols contributes to the success of supervised discriminative methods for semantic labelling of images. Our study makes use of a general and efficient emerging framework, discriminative random forests, for the detection of brain lesions in multi-modal magnetic resonance images (MRIs). The contribution is three-fold. First, we focus on segmentation of brain lesions which is an essential task to diagnosis, prognosis and therapy planning. A context-aware random forest is designed for the automatic multi-class segmentation of MS lesion

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Losch, Max. "Detection and Segmentation of Brain Metastases with Deep Convolutional Networks." Thesis, KTH, Datorseende och robotik, CVAP, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-173519.

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As deep convolutional networks (ConvNets) reach spectacular results on a multitude of computer vision tasks and perform almost as well as a human rater on the task of segmenting gliomas in the brain, I investigated the applicability for detecting and segmenting brain metastases. I trained networks with increasing depth to improve the detection rate and introduced a border-pair-scheme to reduce oversegmentation. A constraint on the time for segmenting a complete brain scan required the utilization of fully convolutional networks which reduced the time from 90 minutes to 40 seconds. Despite some

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11

Chen, Yani. "Deep Learning based 3D Image Segmentation Methods and Applications." Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1547066297047003.

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12

Mazzara, Gloria Patrika. "Brain tumor target volume determination for radiation therapy treatment planning through the use of automated MRI segmentation." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000600.

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13

Raina, Kevin. "Machine Learning Methods for Brain Lesion Delineation." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41156.

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Brain lesions are regions of abnormal or damaged tissue in the brain, commonly due to stroke, cancer or other disease. They are diagnosed primarily using neuroimaging, the most common modalities being Magnetic Resonance Imaging (MRI) or Computed Tomography (CT). Brain lesions have a high degree of variability in terms of location, size, intensity and form, which makes diagnosis challenging. Traditionally, radiologists diagnose lesions by inspecting neuroimages directly by eye; however, this is time-consuming and subjective. For these reasons, many automated methods have been developed fo

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Mohan, Vandana. "Computer vision and machine learning methods for the analysis of brain and cardiac imagery." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/39628.

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Medical imagery is increasingly evolving towards higher resolution and throughput. The increasing volume of data and the usage of multiple and often novel imaging modalities necessitates the use of mathematical and computational techniques for quicker, more accurate and more robust analysis of medical imagery. The fields of computer vision and machine learning provide a rich set of techniques that are useful in medical image analysis, in tasks ranging from segmentation to classification and population analysis, notably by integrating the qualitative knowledge of experts in anatomy and the path

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Alberts, Esther [Verfasser], Björn [Akademischer Betreuer] Menze, Björn [Gutachter] Menze, and Claus [Gutachter] Zimmer. "Multi-modal Multi-temporal Brain Tumor Segmentation, Growth Analysis and Texture-based Classification / Esther Alberts ; Gutachter: Björn Menze, Claus Zimmer ; Betreuer: Björn Menze." München : Universitätsbibliothek der TU München, 2019. http://d-nb.info/118744393X/34.

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16

Ben, Naceur Mostefa. "Deep Neural Networks for the segmentation and classification in Medical Imaging." Thesis, Paris Est, 2020. http://www.theses.fr/2020PESC2014.

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De nos jours, obtenir une segmentation efficace des tumeurs cérébrales de Glioblastome Multiforme (GBM) dans des images IRM multimodale le plus tôt possible, donne un diagnostic clinique, traitement et suivi précoce. La technique d'IRM est conçue spécifiquement pour fournir aux radiologues des outils puissants de visualisation pour analyser des images médicales, mais le challenge réside dans l'interprétation des images radiologiques avec les données cliniques et pathologiques et leurs causes dans les tumeurs GBM. C'est pourquoi la recherche quantitative en neuroimagerie nécessite souvent une s

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Mlynarski, Pawel. "Apprentissage profond pour la segmentation des tumeurs cérébrales et des organes à risque en radiothérapie." Thesis, Université Côte d'Azur (ComUE), 2019. http://www.theses.fr/2019AZUR4084.

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Les images médicales jouent un rôle important dans le diagnostic et la prise en charge des cancers. Les oncologues analysent des images pour déterminer les différentes caractéristiques de la tumeur, pour proposer un traitement adapté et suivre l'évolution de la maladie. L'objectif de cette thèse est de proposer des méthodes efficaces de segmentation automatique des tumeurs cérébrales et des organes à risque dans le contexte de la radiothérapie, à partir des images de résonance magnétique (IRM). Premièrement, nous nous intéressons à la segmentation des tumeurs cérébrales en utilisant des réseau

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Hedman, Karolina. "Differences in tumor volume for treated glioblastoma patients examined with 18F-fluorothymidine PET and contrast-enhanced MRI." Thesis, Umeå universitet, Institutionen för fysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-173693.

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Background: Glioblastoma (GBM) is the most common and malignant primary brain tumor. It is a rapidly progressing tumor that infiltrates the adjacent healthy brain tissue and is difficult to treat. Despite modern treatment including surgical resection followed by radiochemotherapy and adjuvant chemotherapy, the outcome remains poor. The median overall survival is 10-12 months. Neuroimaging is the most important diagnostic tool in the assessment of GBMs and the current imaging standard is contrast-enhanced magnetic resonance imaging (MRI). Positron emission tomography (PET) has been recommended

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19

Dvořák, Pavel. "Detekce a segmentace mozkového nádoru v multisekvenčním MRI." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-233675.

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Tato práce se zabývá detekcí a segmentací mozkového nádoru v multisekvenčních MR obrazech se zaměřením na gliomy vysokého a nízkého stupně malignity. Jsou zde pro tento účel navrženy tři metody. První metoda se zabývá detekcí prezence částí mozkového nádoru v axiálních a koronárních řezech. Jedná se o algoritmus založený na analýze symetrie při různých rozlišeních obrazu, který byl otestován na T1, T2, T1C a FLAIR obrazech. Druhá metoda se zabývá extrakcí oblasti celého mozkového nádoru, zahrnující oblast jádra tumoru a edému, ve FLAIR a T2 obrazech. Metoda je schopna extrahovat mozkový nádor

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Soloh, Roaa. "Graphs and Binary Linear Programming for Natural Object Modeling in Computer Vision." Thesis, Normandie, 2022. https://tel.archives-ouvertes.fr/tel-03867801.

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Dans le monde numérique, les formes bidimensionnelles (2D) et tridimensionnelles (3D) sont importantes pour représenter les objets réels. Leurs applications couvrent un large éventail de domaines, notamment la médecine, l'ingénierie, la sécurité, etc. Considérant l'aspect que les modèles 2D et 3D sont très répandus et parce que les graphes sont de puissants outils de modélisation mathématique utilisés dans une variété de domaines informatiques. Nous cherchons à représenter nos données d'entrée sous forme de graphes afin de bénéficier d'une représentation hautement significative. Dans cette thè

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Foroozandeh, Mehdi. "GAN-Based Synthesis of Brain Tumor Segmentation Data : Augmenting a dataset by generating artificial images." Thesis, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-169863.

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Machine learning applications within medical imaging often suffer from a lack of data, as a consequence of restrictions that hinder the free distribution of patient information. In this project, GANs (generative adversarial networks) are used to generate data synthetically, in an effort to circumvent this issue. The GAN framework PGAN is trained on the brain tumor segmentation dataset BraTS to generate new, synthetic brain tumor masks with the same visual characteristics as the real samples. The image-to-image translation network SPADE is subsequently trained on the image pairs in the real dat

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Yi-HsienWang and 王詣賢. "Brain Tumor Segmentation Using Deep learning from Multi-Contrast MRI." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/234x9m.

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碩士<br>國立成功大學<br>資訊工程學系<br>107<br>With the huge success of deep learning in the field of computer vision, there is rising awareness of its application in medical image. Detection of brain tumor using a segmentation approach is critical in cases, where survival of a subject depends on an accurate and timely clinical diagnosis. Gliomas are the most commonly found tumors having irregular shape and ambiguous boundaries, making them one of the hardest tumors to detect. We present a fully automatic deep learning approach for brain tumor segmentation in multi-contrast magnetic resonance image. The pro

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Huang, I.-Chan, and 黃一展. "Image Segmentation and Three Dimensional Reconstruction of MR Brain Tumor Images." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/33546740863553405591.

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碩士<br>大葉大學<br>工業工程與科技管理學系<br>93<br>Brain Tumor is one of the most common diseases in the central nervous system. Though physicians are able to locate the tumor from the 2D Magnetic Resonance (MR) images, the 3D visualized presentation will provide a more accurate and direct tool for clinical applications. In this research, we developed a Computer Aided Diagnostic (CAD) system to segment and to reconstruct the brain and the tumor for MR brain images for 3D reconstruction and visualization. We apply Active Contours Without Edges (i.e. Active Contours Using Level Sets, ACLS) algorithm to segment

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Yang, Tsai-Ling, and 楊采玲. "Automatic segmentation of brain tumor from MR images using deep learning." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/fqr5e7.

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碩士<br>國立臺灣科技大學<br>電機工程系<br>106<br>Glioma is a common type of tumor in the brain, which begins in the supportive tissue of the brain that contains the glial cells. The median survival for adults is about two to three years, and the diagnosis is dependent on the location and the size of the tumor. In 2017, we participated in the competition of BraTS 2017 in Quebec. The purpose of this competition was the automatic segmentation of gliomas in pre-operative scans by using MR brain images and predicted the survival of the patient. Hope that the technology can help diagnosis. The principal method bas

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Lin, Chun-Chieh, and 林俊杰. "The Regisrtration and Brain Tumor Segmentation of Sugical Navigation System for Head." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/38180140306996351170.

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Mamba, Mpendulo, and Mpendulo Mamba. "Automatic Brain Tumor Segmentation with a 3-Dimensional Generative Adversarial Neural Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/4m9m82.

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碩士<br>國立臺北科技大學<br>電資國際專班<br>106<br>Brain tumor segmentation is a very crucial task in medical image processing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumors for cancer diagnosis, from large amounts of magnetic resonance images (MRI) generated in clinical routine, is a difficult and time consuming task. There is a need for automatic brain image segmentation. In this work, we demonstrate a deep neural network for volumetric segmentation that learns from a serie

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KUMAR, SHIKHAR. "INTEGRATING U-NET CNN FOR MRI BRAIN TUMOR SEGMENTATION AND SURVIVAL PREDICTION:A DEEP LEARNING APPROACH." Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/20034.

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Brain tumor segmentation is a difficult task in medical image processing that is essential for the detection and planning of brain cancer. Brain tumor imaging frequently uses magnetic resonance imaging (MRI), but manually segmenting tumors from MRI data is a laborious and subjective operation. In this research, I suggest employing a 3D U-Net convolutional neural network (CNN) architecture and deep learning to automatically segment brain tumors. I also incorporate the segmented tumor volume and patient clinical information into a Cox regression analysis survival prediction model. With a Dice co

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Lopes, Ana Patrícia Ribeiro. "Study of Deep Neural Network architectures for medical image segmentation." Master's thesis, 2020. http://hdl.handle.net/1822/69850.

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Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)<br>Medical image segmentation plays a crucial role in the medical field, since it allows performing quantitative analyses used for screening, monitoring and planning the treatment of numerous pathologies. Manual segmentation is time-consuming and prone to inter-rater variability. Thus, several automatic approaches have been proposed for medical image segmentation and most are based on Deep Learning. These approaches became specially relevant after the development of the Fully Convolution

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(11197152), Somosmita Mitra. "Multi Planar Conditional Generative Adversarial Networks." Thesis, 2021.

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<div>Brain tumor sub region segmentation is a challenging problem in Magnetic Resonance imaging. The tumor regions tend to suffer from lack of homogeneity, textural differences, variable location, and their ability to proliferate into surrounding tissue. </div><div> The segmentation task thus requires an algorithm which can be indifferent to such influences and robust to external interference. In this work we propose a conditional generative adversarial network which learns off multiple planes of reference. Using this learning, we evaluate the quality of the segmentation and back propagate th

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Ribeiro, Alexandrine. "Study of attention mechanisms and ensemble methods for medical image semantic segmentation." Master's thesis, 2019. http://hdl.handle.net/1822/69956.

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Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)<br>Nowadays, the development of medical care and the improvements in medical imaging techniques ensure a better diagnosis capability and a better identification of health problems of difficult treatment. Time is a critical factor for medical diagnosis, and early detection and evaluation can potentially add years to a patient’s life. Over the past years, automatic medical image segmentation has proven to be a viable and robust method to overcome the large costs of human resources and the

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