| 2. |
- Brudfors, Mikael, et al.
(författare)
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Towards real-time, tracker-less 3D ultrasound guidance for spine anaesthesia
- 2015
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Ingår i: International Journal of Computer Assisted Radiology and Surgery. - : Springer Science and Business Media LLC. - 1861-6410 .- 1861-6429. ; 10:6, s. 855-865
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Epidural needle insertions and facet joint injections play an important role in spine anaesthesia. The main challenge of safe needle insertion is the deep location of the target, resulting in a narrow and small insertion channel close to sensitive anatomy. Recent approaches utilizing ultrasound (US) as a low-cost and widely available guiding modality are promising but have yet to become routinely used in clinical practice due to the difficulty in interpreting US images, their limited view of the internal anatomy of the spine, and/or inclusion of cost-intensive tracking hardware which impacts the clinical workflow. Methods: We propose a novel guidance system for spine anaesthesia. An efficient implementation allows us to continuously align and overlay a statistical model of the lumbar spine on the live 3D US stream without making use of additional tracking hardware. The system is evaluated in vivo on 12 volunteers. Results: The in vivo study showed that the anatomical features of the epidural space and the facet joints could be continuously located, at a volume rate of 0.5 Hz, within an accuracy of 3 and 7 mm, respectively. Conclusions: A novel guidance system for spine anaesthesia has been presented which augments a live 3D US stream with detailed anatomical information of the spine. Results from an in vivo study indicate that the proposed system has potential for assisting the physician in quickly finding the target structure and planning a safe insertion trajectory in the spine.
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| 3. |
- Hering, Alessa, et al.
(författare)
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Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
- 2023
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Ingår i: IEEE Transactions on Medical Imaging. - : Institute of Electrical and Electronics Engineers (IEEE). - 0278-0062 .- 1558-254X. ; 42:3, s. 697-712
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Tidskriftsartikel (refereegranskat)abstract
- Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https:// learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods.
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