TY - GEN
T1 - State-of-the-Art in Brain Tumor Segmentation and Current Challenges
AU - Yousaf, Sobia
AU - RaviPrakash, Harish
AU - Anwar, Syed Muhammad
AU - Sohail, Nosheen
AU - Bagci, Ulas
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85101582720&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-66843-3_19
DO - 10.1007/978-3-030-66843-3_19
M3 - Conference contribution
AN - SCOPUS:85101582720
SN - 9783030668426
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 189
EP - 198
BT - Machine 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
A2 - Kia, Seyed Mostafa
A2 - Mohy-ud-Din, Hassan
A2 - Abdulkadir, Ahmed
A2 - Bass, Cher
A2 - Habes, Mohamad
A2 - Rondina, Jane Maryam
A2 - Tax, Chantal
A2 - Wang, Hongzhi
A2 - Wolfers, Thomas
A2 - Rathore, Saima
A2 - Ingalhalikar, Madhura
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd 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
Y2 - 4 October 2020 through 8 October 2020
ER -
