Sökning: onr:"swepub:oai:DiVA.org:uu-525493" >
3-D Attention-SEV-N...
3-D Attention-SEV-Net for Segmentation of Post-operative Glioblastoma with Interactive Correction of Over-Segmentation
-
- Kundu, Swagata (författare)
- Natl Inst Technol Durgapur, Elect Engn Dept, Mahatma Gandhi Ave, Durgapur 713209, West Bengal, India.
-
- Banerjee, Subhashis (författare)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen Vi3
-
- Toumpanakis, Dimitrios (författare)
- Uppsala universitet,Institutionen för kirurgiska vetenskaper,Neuroradiologi
-
visa fler...
-
- Wikström, Johan, Professor, 1964- (författare)
- Uppsala universitet,Institutionen för kirurgiska vetenskaper,Neuroradiologi
-
- Strand, Robin, 1978- (författare)
- Uppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen Vi3
-
- Dhara, Ashis Kumar (författare)
- Natl Inst Technol Durgapur, Elect Engn Dept, Mahatma Gandhi Ave, Durgapur 713209, West Bengal, India.
-
visa färre...
-
Natl Inst Technol Durgapur, Elect Engn Dept, Mahatma Gandhi Ave, Durgapur 713209, West Bengal, India Bildanalys och människa-datorinteraktion (creator_code:org_t)
- Springer, 2023
- 2023
- Engelska.
-
Ingår i: Pattern Recognition and Machine Intelligence, PREMI 2023. - : Springer. - 9783031451690 - 9783031451706 ; , s. 380-387
- Relaterad länk:
-
https://urn.kb.se/re...
-
visa fler...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- Accurate localization and volumetric quantification of postoperative glioblastoma are of profound importance for clinical applications like post-surgery treatment planning, monitoring of tumor regrowth, and radiotherapy map planning. Manual delineation consumes more time and error prone thus automated 3-D quantification of brain tumors using deep learning algorithms from MRI scans has been used in recent years. The shortcoming with automated segmentation is that it often over-segments or under-segments the tumor regions. An interactive deep-learning tool will enable radiologists to correct the over-segmented and under-segmented voxels. In this paper, we proposed a network named Attention-SEV-Net which outperforms state-of-the-art network architectures. We also developed an interactive graphical user interface, where the initial 3-D segmentation of contrast-enhanced tumor can be interactively corrected to remove falsely detected isolated tumor regions. Attention-SEV-Net is trained with BraTS-2021 training data set and tested on Uppsala University post-operative glioblastoma dataset. The methodology outperformed state-of-the-art networks like U-Net, VNet, Attention U-Net and Residual U-Net. The mean dice score achieved is 0.6682 and the mean Hausdorff distance-95 got is 8.96mm for the Uppsala University dataset.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
Nyckelord
- Attention-SEV-Net
- Post-operative Glioblastoma
- Interactive Correction
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
Hitta via bibliotek
Till lärosätets databas