Ramalhinho, J., Koo, B., Montaña-Brown, N., Saeed, S.U., Bonmati Coll, E., Gurusamy, K., Pereira, S.P., Davidson, B., Hu, Y. and Clarkson, M.J. 2022. Deep hashing for global registration of untracked 2D laparoscopic ultrasound to CT. International Journal of Computer Assisted Radiology and Surgery. 17, pp. 1461-1468. https://doi.org/10.1007/s11548-022-02605-3
| Title | Deep hashing for global registration of untracked 2D laparoscopic ultrasound to CT |
|---|---|
| Type | Journal article |
| Authors | Ramalhinho, J., Koo, B., Montaña-Brown, N., Saeed, S.U., Bonmati Coll, E., Gurusamy, K., Pereira, S.P., Davidson, B., Hu, Y. and Clarkson, M.J. |
| 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. |
| Keywords | Convolutional neural networks |
| Deep hashing | |
| Laparoscopic ultrasound | |
| Multi-modal registration | |
| Journal | International Journal of Computer Assisted Radiology and Surgery |
| Journal citation | 17, pp. 1461-1468 |
| ISSN | 1861-6429 |
| Year | 2022 |
| Publisher | Springer Nature |
| Publisher's version | License CC BY 4.0 File Access Level Open (open metadata and files) |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s11548-022-02605-3 |
| Web address (URL) | https://link.springer.com/article/10.1007/s11548-022-02605-3 |
| Publication dates | |
| Published | 02 Apr 2022 |