Fully automated brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning.

Ermis, Ekin; Jungo, Alain; Poel, Robert; Blatti-Moreno, Marcela; Meier, Raphael; Knecht, Urspeter; Aebersold, Daniel M.; Fix, Michael K.; Manser, Peter; Reyes, Mauricio; Herrmann, Evelyn (2020). Fully automated brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning. Radiation oncology, 15(1), p. 100. BioMed Central 10.1186/s13014-020-01553-z

[img]
Preview
Text
13014_2020_Article_1553.pdf - Published Version
Available under License Creative Commons: Attribution (CC-BY).

Download (2MB) | Preview

BACKGROUND Automated brain tumor segmentation methods are computational algorithms that yield tumor delineation from, in this case, multimodal magnetic resonance imaging (MRI). We present an automated segmentation method and its results for resection cavity (RC) in glioblastoma multiforme (GBM) patients using deep learning (DL) technologies. METHODS Post-operative, T1w with and without contrast, T2w and fluid attenuated inversion recovery MRI studies of 30 GBM patients were included. Three radiation oncologists manually delineated the RC to obtain a reference segmentation. We developed a DL cavity segmentation method, which utilizes all four MRI sequences and the reference segmentation to learn to perform RC delineations. We evaluated the segmentation method in terms of Dice coefficient (DC) and estimated volume measurements. RESULTS Median DC of the three radiation oncologist were 0.85 (interquartile range [IQR]: 0.08), 0.84 (IQR: 0.07), and 0.86 (IQR: 0.07). The results of the automatic segmentation compared to the three different raters were 0.83 (IQR: 0.14), 0.81 (IQR: 0.12), and 0.81 (IQR: 0.13) which was significantly lower compared to the DC among raters (chi-square = 11.63, p = 0.04). We did not detect a statistically significant difference of the measured RC volumes for the different raters and the automated method (Kruskal-Wallis test: chi-square = 1.46, p = 0.69). The main sources of error were due to signal inhomogeneity and similar intensity patterns between cavity and brain tissues. CONCLUSIONS The proposed DL approach yields promising results for automated RC segmentation in this proof of concept study. Compared to human experts, the DC are still subpar.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Clinic of Radiation Oncology > Medical Radiation Physics
04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Clinic of Radiation Oncology
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research

UniBE Contributor:

Ermis, Ekin; Jungo, Alain; Poel, Robert; Blatti, Marcela Judith; Meier, Raphael; Knecht, Urspeter; Aebersold, Daniel; Fix, Michael; Manser, Peter; Reyes, Mauricio and Herrmann, Evelyn

Subjects:

600 Technology > 610 Medicine & health
500 Science > 570 Life sciences; biology

ISSN:

1748-717X

Publisher:

BioMed Central

Language:

English

Submitter:

Beatrice Scheidegger

Date Deposited:

26 May 2020 15:27

Last Modified:

31 May 2020 02:45

Publisher DOI:

10.1186/s13014-020-01553-z

PubMed ID:

32375839

Uncontrolled Keywords:

Automatic segmentation Deep learning Glioblastoma MRI Target definition

BORIS DOI:

10.7892/boris.143987

URI:

https://boris.unibe.ch/id/eprint/143987

Actions (login required)

Edit item Edit item
Provide Feedback