Name
Title | ||
|---|---|---|
Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using deep convolutional neural networks. |
Abstract | ||
|---|---|---|
•A DCNN-based fully automatic thrombus detection and segmentation pipeline that is easily translatable to clinical practice is proposed.•A new DCNN architecture adapted to post-operative thrombus segmentation from CTA images is presented, which combines low level features with coarser representations.•The well-known Detectnet computer vision network is translated into the clinical domain, specifically for thrombus region of interest detection in CTA images.•Automatic segmentation exceeds previous state of the art results, with a mean Dice similarity coefficient of 82%.•In terms of clinical applicability, the obtained segmentation results lay within the experienced human observer variance without the need of human intervention in most common cases. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.media.2018.03.010 | Medical Image Analysis |
Keywords | DocType | Volume |
AAA,EVAR,Segmentation,DCNN,Deep learning,Thrombus,Post-operative,detection | Journal | 46 |
ISSN | Citations | PageRank |
1361-8415 | 4 | 0.46 |
References | Authors | |
18 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karen López-Linares | 1 | 6 | 2.86 |
| Nerea Aranjuelo | 2 | 4 | 1.47 |
| Luis Kabongo | 3 | 36 | 4.27 |
| G Maclair | 4 | 11 | 3.00 |
| Nerea Lete | 5 | 5 | 1.17 |
| Mario Ceresa | 6 | 22 | 9.97 |
| Ainhoa García-Familiar | 7 | 4 | 0.46 |
| I. Macia | 8 | 53 | 9.32 |
| Miguel Ángel González Ballester | 9 | 212 | 34.31 |