| 000 | 04796nlm1a2200625 4500 | ||
|---|---|---|---|
| 001 | 667326 | ||
| 005 | 20231030042107.0 | ||
| 035 | _a(RuTPU)RU\TPU\network\38531 | ||
| 035 | _aRU\TPU\network\36495 | ||
| 090 | _a667326 | ||
| 100 | _a20220316a2021 k y0engy50 ba | ||
| 101 | 0 | _aeng | |
| 102 | _aCH | ||
| 135 | _adrcn ---uucaa | ||
| 181 | 0 | _ai | |
| 182 | 0 | _ab | |
| 200 | 1 |
_aAortography Keypoint Tracking for Transcatheter Aortic Valve Implantation Based on Multi-Task Learning _fV. V. Danilov, K. Yu. Klyshnikov, O. M. Gerget [et al.] |
|
| 203 |
_aText _celectronic |
||
| 300 | _aTitle screen | ||
| 320 | _a[References: 34 tit.]. | ||
| 330 | _aCurrently, transcatheter aortic valve implantation (TAVI) represents the most efficient treatment option for patients with aortic stenosis, yet its clinical outcomes largely depend on the accuracy of valve positioning that is frequently complicated when routine imaging modalities are applied. Therefore, existing limitations of perioperative imaging underscore the need for the development of novel visual assistance systems enabling accurate procedures. In this paper, we propose an original multi-task learning-based algorithm for tracking the location of anatomical landmarks and labeling critical keypoints on both aortic valve and delivery system during TAVI. In order to optimize the speed and precision of labeling, we designed nine neural networks and then tested them to predict 11 keypoints of interest. These models were based on a variety of neural network architectures, namely MobileNet V2, ResNet V2, Inception V3, Inception ResNet V2 and EfficientNet B5. During training and validation, ResNet V2 and MobileNet V2 architectures showed the best prediction accuracy/time ratio, predicting keypoint labels and coordinates with 97/96% accuracy and 4.7/5.6% mean absolute error, respectively. Our study provides evidence that neural networks with these architectures are capable to perform real-time predictions of aortic valve and delivery system location, thereby contributing to the proper valve positioning during TAVI. | ||
| 338 |
_bРоссийский научный фонд _d18-75-10061 |
||
| 461 | _tFrontiers in Cardiovascular Medicine | ||
| 463 |
_tVol. 8 _v[697737, 15 p.] _d2021 |
||
| 610 | 1 | _aэлектронный ресурс | |
| 610 | 1 | _aтруды учёных ТПУ | |
| 610 | 1 | _akeypoint tracking | |
| 610 | 1 | _amulti-task learning | |
| 610 | 1 | _atranscatheter aortic valve replacement | |
| 610 | 1 | _adeep learning-CNN | |
| 610 | 1 | _amedical image analysis | |
| 610 | 1 | _aaortography | |
| 610 | 1 | _aотслеживание | |
| 610 | 1 | _aключевые точки | |
| 610 | 1 | _aзамена | |
| 610 | 1 | _aклапаны | |
| 610 | 1 | _aмедицинские изображения | |
| 610 | 1 | _aаортография | |
| 610 | 1 | _aимплантация | |
| 610 | 1 | _aвизуализация | |
| 610 | 1 | _aмногозадачное обучение | |
| 701 | 1 |
_aDanilov _bV. V. _cspecialist in the field of informatics and computer technology _cengineer of Tomsk Polytechnic University _f1989- _gVyacheslav Vladimirovich _2stltpush _3(RuTPU)RU\TPU\pers\37831 |
|
| 701 | 1 |
_aKlyshnikov _bK. Yu. _gKirill Yurjevich |
|
| 701 | 1 |
_aGerget _bO. M. _cSpecialist in the field of informatics and computer technology _cProfessor of Tomsk Polytechnic University, Doctor of Sciences _f1974- _gOlga Mikhailovna _2stltpush _3(RuTPU)RU\TPU\pers\31430 |
|
| 701 | 1 |
_aSkirnevsky _bI. P. _cspecialist in the field of automation and computer systems _ceducational master Tomsk Polytechnic University _f1989- _gIgor Petrovich _2stltpush _3(RuTPU)RU\TPU\pers\35105 |
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| 701 | 1 |
_aKutikhin _bA. G. _gAnton Gennadievich |
|
| 701 | 1 |
_aShilov _bA. A. _gAleksandr |
|
| 701 | 1 |
_aGanuykov _bV. I. _gVladimir |
|
| 701 | 1 |
_aOvcharenko _bE. A. _gEvgeny Andreevich |
|
| 712 | 0 | 2 |
_aНациональный исследовательский Томский политехнический университет _bИнженерная школа информационных технологий и робототехники _bНаучно-образовательная лаборатория обработки и анализа больших данных _h7959 _2stltpush _3(RuTPU)RU\TPU\col\23599 |
| 712 | 0 | 2 |
_aНациональный исследовательский Томский политехнический университет _bИнженерная школа информационных технологий и робототехники _bОтделение информационных технологий _h7951 _2stltpush _3(RuTPU)RU\TPU\col\23515 |
| 801 | 2 |
_aRU _b63413507 _c20220505 _gRCR |
|
| 856 | 4 | _uhttp://earchive.tpu.ru/handle/11683/70713 | |
| 856 | 4 | _uhttps://doi.org/10.3389/fcvm.2021.697737 | |
| 942 | _cCF | ||