Deep hashing for global registration of untracked 2D laparoscopic ultrasound to CT.
Int J Comput Assist Radiol Surg
; 17(8): 1461-1468, 2022 Aug.
Article
em En
| MEDLINE
| ID: mdl-35366130
ABSTRACT
PURPOSE:
The registration of Laparoscopic Ultrasound (LUS) to CT can enhance the safety of laparoscopic liver surgery by providing the surgeon with awareness on the relative positioning between critical vessels and a tumour. In an effort to provide a translatable solution for this poorly constrained problem, Content-based Image Retrieval (CBIR) based on vessel information has been suggested as a method for obtaining a global coarse registration without using tracking information. However, the performance of these frameworks is limited by the use of non-generalisable handcrafted vessel features.METHODS:
We propose the use of a Deep Hashing (DH) network to directly convert vessel images from both LUS and CT into fixed size hash codes. During training, these codes are learnt from a patient-specific CT scan by supplying the network with triplets of vessel images which include both a registered and a mis-registered pair. Once hash codes have been learnt, they can be used to perform registration with CBIR methods.RESULTS:
We test a CBIR pipeline on 11 sequences of untracked LUS distributed across 5 clinical cases. Compared to a handcrafted feature approach, our model improves the registration success rate significantly from 48% to 61%, considering a 20 mm error as the threshold for a successful coarse registration.CONCLUSIONS:
We present the first DH framework for interventional multi-modal registration tasks. The presented approach is easily generalisable to other registration problems, does not require annotated data for training, and may promote the translation of these techniques.Palavras-chave
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Base de dados:
MEDLINE
Assunto principal:
Tomografia Computadorizada por Raios X
/
Laparoscopia
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article