State-of-the-Art in Brain Tumor Segmentation and Current Challenges

Sobia Yousaf, Harish RaviPrakash, Syed Muhammad Anwar*, Nosheen Sohail, Ulas Bagci

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Brain tumors are the third most common type of cancer among young adults and an accurate diagnosis and treatment demands strict delineation of the tumor effected tissue. Brain tumor segmentation involves segmenting different tumor tissues, particularly, the enhancing tumor regions, non-enhancing tumor and necrotic regions, and edema. With increasing computational power and data sharing, computer vision algorithms, particularly deep learning approaches, have begun to dominate the field of medical image segmentation. Accurate tumor segmentation will help in surgery planning as well as monitor the progress in longitudinal studies enabling a better understanding of the factors effecting malignant growth. The objective of this paper is to provide an overview of the current state-of-the-art in brain tumor segmentation approaches, an idea of the available resources, and highlight the most promising research directions moving forward. We also intend to highlight the challenges that exist in this field, in particular towards the successful adoption of such methods to clinical practice.

Original languageEnglish (US)
Title of host publicationMachine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology - 3rd International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020, Held in Conjunction with MICCAI 2020, Proceedings
EditorsSeyed Mostafa Kia, Hassan Mohy-ud-Din, Ahmed Abdulkadir, Cher Bass, Mohamad Habes, Jane Maryam Rondina, Chantal Tax, Hongzhi Wang, Thomas Wolfers, Saima Rathore, Madhura Ingalhalikar
PublisherSpringer Science and Business Media Deutschland GmbH
Pages189-198
Number of pages10
ISBN (Print)9783030668426
DOIs
StatePublished - 2020
Externally publishedYes
Event3rd International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and 2nd International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020 - Lima, Peru
Duration: Oct 4 2020Oct 8 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12449 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and 2nd International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020
Country/TerritoryPeru
CityLima
Period10/4/2010/8/20

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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