| Title |
Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI
|
|---|---|
| Published in |
European Radiology, June 2018
|
| DOI | 10.1007/s00330-018-5595-8 |
| Pubmed ID | |
| Authors |
Kai Roman Laukamp, Frank Thiele, Georgy Shakirin, David Zopfs, Andrea Faymonville, Marco Timmer, David Maintz, Michael Perkuhn, Jan Borggrefe |
| Abstract |
Magnetic resonance imaging (MRI) is the method of choice for imaging meningiomas. Volumetric assessment of meningiomas is highly relevant for therapy planning and monitoring. We used a multiparametric deep-learning model (DLM) on routine MRI data including images from diverse referring institutions to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentations. We included 56 of 136 consecutive preoperative MRI datasets [T1/T2-weighted, T1-weighted contrast-enhanced (T1CE), FLAIR] of meningiomas that were treated surgically at the University Hospital Cologne and graded histologically as tumour grade I (n = 38) or grade II (n = 18). The DLM was trained on an independent dataset of 249 glioma cases and segmented different tumour classes as defined in the brain tumour image segmentation benchmark (BRATS benchmark). The DLM was based on the DeepMedic architecture. Results were compared to manual segmentations by two radiologists in a consensus reading in FLAIR and T1CE. The DLM detected meningiomas in 55 of 56 cases. Further, automated segmentations correlated strongly with manual segmentations: average Dice coefficients were 0.81 ± 0.10 (range, 0.46-0.93) for the total tumour volume (union of tumour volume in FLAIR and T1CE) and 0.78 ± 0.19 (range, 0.27-0.95) for contrast-enhancing tumour volume in T1CE. The DLM yielded accurate automated detection and segmentation of meningioma tissue despite diverse scanner data and thereby may improve and facilitate therapy planning as well as monitoring of this highly frequent tumour entity. • Deep learning allows for accurate meningioma detection and segmentation • Deep learning helps clinicians to assess patients with meningiomas • Meningioma monitoring and treatment planning can be improved. |
X Demographics
The data shown below were collected from the profiles of 15 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 9 | 60% |
| Austria | 2 | 13% |
| Netherlands | 1 | 7% |
| Unknown | 3 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 5 | 33% |
| Members of the public | 4 | 27% |
| Science communicators (journalists, bloggers, editors) | 4 | 27% |
| Scientists | 2 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 217 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 217 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 28 | 13% |
| Student > Master | 24 | 11% |
| Student > Bachelor | 20 | 9% |
| Student > Ph. D. Student | 18 | 8% |
| Other | 12 | 6% |
| Other | 36 | 17% |
| Unknown | 79 | 36% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 44 | 20% |
| Computer Science | 27 | 12% |
| Engineering | 16 | 7% |
| Agricultural and Biological Sciences | 7 | 3% |
| Neuroscience | 5 | 2% |
| Other | 25 | 12% |
| Unknown | 93 | 43% |
Attention Score in Context
This research output has an Altmetric Attention Score of 20. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 January 2020.
All research outputs
#1,602,148
of 23,094,276 outputs
Outputs from European Radiology
#111
of 4,183 outputs
Outputs of similar age
#36,141
of 328,989 outputs
Outputs of similar age from European Radiology
#5
of 90 outputs
Altmetric has tracked 23,094,276 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,183 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 328,989 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 90 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.