AI-based MRI auto-segmentation of brain tumor in rodents, a multicenter study
© 2023. The Author(s)..
Automatic segmentation of rodent brain tumor on magnetic resonance imaging (MRI) may facilitate biomedical research. The current study aims to prove the feasibility for automatic segmentation by artificial intelligence (AI), and practicability of AI-assisted segmentation. MRI images, including T2WI, T1WI and CE-T1WI, of brain tumor from 57 WAG/Rij rats in KU Leuven and 46 mice from the cancer imaging archive (TCIA) were collected. A 3D U-Net architecture was adopted for segmentation of tumor bearing brain and brain tumor. After training, these models were tested with both datasets after Gaussian noise addition. Reduction of inter-observer disparity by AI-assisted segmentation was also evaluated. The AI model segmented tumor-bearing brain well for both Leuven and TCIA datasets, with Dice similarity coefficients (DSCs) of 0.87 and 0.85 respectively. After noise addition, the performance remained unchanged when the signal-noise ratio (SNR) was higher than two or eight, respectively. For the segmentation of tumor lesions, AI-based model yielded DSCs of 0.70 and 0.61 for Leuven and TCIA datasets respectively. Similarly, the performance is uncompromised when the SNR was over two and eight respectively. AI-assisted segmentation could significantly reduce the inter-observer disparities and segmentation time in both rats and mice. Both AI models for segmenting brain or tumor lesions could improve inter-observer agreement and therefore contributed to the standardization of the following biomedical studies.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:11 |
|---|---|
| Enthalten in: |
Acta neuropathologica communications - 11(2023), 1 vom: 14. Jan., Seite 11 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Wang, Shuncong [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Artificial intelligence |
|---|
| Anmerkungen: |
Date Completed 17.01.2023 Date Revised 02.02.2023 published: Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1186/s40478-023-01509-w |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM351523790 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM351523790 | ||
| 003 | DE-627 | ||
| 005 | 20231226051555.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1186/s40478-023-01509-w |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1171.xml |
| 035 | |a (DE-627)NLM351523790 | ||
| 035 | |a (NLM)36641470 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Wang, Shuncong |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a AI-based MRI auto-segmentation of brain tumor in rodents, a multicenter study |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 17.01.2023 | ||
| 500 | |a Date Revised 02.02.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023. The Author(s). | ||
| 520 | |a Automatic segmentation of rodent brain tumor on magnetic resonance imaging (MRI) may facilitate biomedical research. The current study aims to prove the feasibility for automatic segmentation by artificial intelligence (AI), and practicability of AI-assisted segmentation. MRI images, including T2WI, T1WI and CE-T1WI, of brain tumor from 57 WAG/Rij rats in KU Leuven and 46 mice from the cancer imaging archive (TCIA) were collected. A 3D U-Net architecture was adopted for segmentation of tumor bearing brain and brain tumor. After training, these models were tested with both datasets after Gaussian noise addition. Reduction of inter-observer disparity by AI-assisted segmentation was also evaluated. The AI model segmented tumor-bearing brain well for both Leuven and TCIA datasets, with Dice similarity coefficients (DSCs) of 0.87 and 0.85 respectively. After noise addition, the performance remained unchanged when the signal-noise ratio (SNR) was higher than two or eight, respectively. For the segmentation of tumor lesions, AI-based model yielded DSCs of 0.70 and 0.61 for Leuven and TCIA datasets respectively. Similarly, the performance is uncompromised when the SNR was over two and eight respectively. AI-assisted segmentation could significantly reduce the inter-observer disparities and segmentation time in both rats and mice. Both AI models for segmenting brain or tumor lesions could improve inter-observer agreement and therefore contributed to the standardization of the following biomedical studies | ||
| 650 | 4 | |a Multicenter Study | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Brain malignancy | |
| 650 | 4 | |a MRI | |
| 650 | 4 | |a Rodent | |
| 650 | 4 | |a Segmentation | |
| 700 | 1 | |a Pang, Xin |e verfasserin |4 aut | |
| 700 | 1 | |a de Keyzer, Frederik |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, Yuanbo |e verfasserin |4 aut | |
| 700 | 1 | |a Swinnen, Johan V |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Jie |e verfasserin |4 aut | |
| 700 | 1 | |a Ni, Yicheng |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Acta neuropathologica communications |d 2013 |g 11(2023), 1 vom: 14. Jan., Seite 11 |w (DE-627)NLM230788246 |x 2051-5960 |7 nnns |
| 773 | 1 | 8 | |g volume:11 |g year:2023 |g number:1 |g day:14 |g month:01 |g pages:11 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1186/s40478-023-01509-w |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 11 |j 2023 |e 1 |b 14 |c 01 |h 11 | ||