Name
Abstract | ||
|---|---|---|
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. 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. 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. 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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s11548-022-02605-3 | International Journal of Computer Assisted Radiology and Surgery |
Keywords | DocType | Volume |
Laparoscopic ultrasound, Multi-modal registration, Convolutional neural networks, Deep hashing | Journal | 17 |
Issue | ISSN | Citations |
8 | 1861-6429 | 0 |
PageRank | References | Authors |
0.34 | 9 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| João Ramalhinho | 1 | 0 | 0.34 |
| Bongjin Koo | 2 | 0 | 0.34 |
| Nina Montaña-Brown | 3 | 0 | 0.34 |
| Shaheer U Saeed | 4 | 0 | 0.34 |
| Ester Bonmati | 5 | 60 | 4.77 |
| Kurinchi Gurusamy | 6 | 78 | 9.01 |
| Stephen P Pereira | 7 | 0 | 0.34 |
| Brian Davidson | 8 | 0 | 0.68 |
| Yipeng Hu | 9 | 0 | 0.34 |
| Matthew J. Clarkson | 10 | 385 | 39.15 |