Accurate Segmentation of Brain Tumors in Magnetic Resonance Images with Pyramid Stage Decomposition Network Approach

被引：0

作者：

Ari, Berna Gurler ^{[1
]}

Uzen, Huseyin ^{[2
]}

Sengur, Abdulkadir ^{[3
]}

机构：

[1] Inonu Univ, Bilgisayar Muhendisligi Bolumu, Malatya, Turkiye

[2] Bingo Univ, Bilgisayar Muhendisligi Bolumu, Bingo, Turkiye

[3] Firat Univ, Elekt Elekt Teknol Bolumu, Elazig, Turkiye

来源：

32ND IEEE SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, SIU 2024 | 2024年

关键词：

Brain tumors; Magnetic Resonance Imaging (MRI); Segmentation; Pyramid Scene Parsing Network (PSPNet); Medical Image Analysis;

D O I：

10.1109/SIU61531.2024.10600920

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This study explores the utilization of the Pyramid Scene Parsing Network (PSPNet) architecture to achieve accurate segmentation of brain tumors in magnetic resonance (MR) images. Experimental evaluations were conducted on different pre-trained backbone network models, including Vgg16, Inceptionv3, Mobilenetv2, Efficientnetb0, Resnet18, Resnet34, Resnet50, Resnet101, Resnext50, and Resnext101, assessing the performance of each model in brain tumor segmentation. The results highlight the VGG16-PSPNet model as the most successful, showcasing high F1-score, mIoU, precision, recall, and accuracy values.

引用

页数：4

共 50 条

[41] Encoder Modified U-Net and Feature Pyramid Network for Multi-class Segmentation of Cardiac Magnetic Resonance Images
Sharan, Taresh Sarvesh
Tripathi, Sumit
Sharma, Shiru
Sharma, Neeraj
IETE TECHNICAL REVIEW, 2022, 39 (05) : 1092 - 1104
[42] A Deep Neural Network Approach for the Lesion Segmentation from Neonatal Brain Magnetic Resonance Imaging
Tahmasebi, Nazanin
Punithakumar, Kumaradevan
AI FOR BRAIN LESION DETECTION AND TRAUMA VIDEO ACTION RECOGNITION, BONBID-HIE 2023, TTC 2023, 2025, 14567 : 34 - 38
[43] Automated segmentation of the corpus callosum in midsagittal brain magnetic resonance images
Lee, C
Huh, S
Ketter, TA
Unser, M
OPTICAL ENGINEERING, 2000, 39 (04) : 924 - 935
[44] Level Set Formulation Based on Edge and Region Information with Application to Accurate Lesion Segmentation of Brain Magnetic Resonance Images
Yang, Yunyun
Jia, Wenjing
Shu, Xiu
Wu, Boying
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS, 2019, 182 (02) : 797 - 815
[45] Model for Enhancement and Segmentation of Magnetic Resonance Images for Brain Tumor Classification
A. M. Chikhalikar
N. V. Dharwadkar
Pattern Recognition and Image Analysis, 2021, 31 : 49 - 59
[46] Model for Enhancement and Segmentation of Magnetic Resonance Images for Brain Tumor Classification
Chikhalikar, A. M.
Dharwadkar, N., V
PATTERN RECOGNITION AND IMAGE ANALYSIS, 2021, 31 (01) : 49 - 59
[47] Segmentation of Myelinated White Matter in Pediatric Brain Magnetic Resonance Images
Devi, Chelli N.
Sundararaman, V. K.
Chandrasekharan, Anupama
Alex, Zachariah C.
2014 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMPUTING RESEARCH (IEEE ICCIC), 2014, : 726 - 729
[48] Level Set Formulation Based on Edge and Region Information with Application to Accurate Lesion Segmentation of Brain Magnetic Resonance Images
Yunyun Yang
Wenjing Jia
Xiu Shu
Boying Wu
Journal of Optimization Theory and Applications, 2019, 182 : 797 - 815
[49] A review of atlas-based segmentation for magnetic resonance brain images
Cabezas, Mariano
Oliver, Arnau
Llado, Xavier
Freixenet, Jordi
Cuadra, Meritxell Bach
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2011, 104 (03) : E158 - E177
[50] Three-dimensional segmentation and interpolation of magnetic resonance brain images
Joliot, Marc, 1600, (12):

← 1 2 3 4 5 →