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- van der Putten, J, et al.
(författare)
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Endoscopy-Driven Pretraining for Classification of Dysplasia in Barrett's Esophagus with Endoscopic Narrow-Band Imaging Zoom Videos
- 2020
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Ingår i: APPLIED SCIENCES-BASEL. - : MDPI AG. - 2076-3417. ; 10:10
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Endoscopic diagnosis of early neoplasia in Barrett’s Esophagus is generally a two-step process of primary detection in overview, followed by detailed inspection of any visible abnormalities using Narrow Band Imaging (NBI). However, endoscopists struggle with evaluating NBI-zoom imagery of subtle abnormalities. In this work, we propose the first results of a deep learning system for the characterization of NBI-zoom imagery of Barrett’s Esophagus with an accuracy, sensitivity, and specificity of 83.6%, 83.1%, and 84.0%, respectively. We also show that endoscopy-driven pretraining outperforms two models, one without pretraining as well as a model with ImageNet initialization. The final model outperforms absence of pretraining by approximately 10% and the performance is 2% higher in terms of accuracy compared to ImageNet pretraining. Furthermore, the practical deployment of our model is not hampered by ImageNet licensing, thereby paving the way for clinical application.
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- Lai, M, et al.
(författare)
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Development of a CT-Compatible, Anthropomorphic Skull and Brain Phantom for Neurosurgical Planning, Training, and Simulation
- 2022
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Ingår i: Bioengineering (Basel, Switzerland). - : MDPI AG. - 2306-5354. ; 9:10
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Tidskriftsartikel (refereegranskat)abstract
- Background: Neurosurgical procedures are complex and require years of training and experience. Traditional training on human cadavers is expensive, requires facilities and planning, and raises ethical concerns. Therefore, the use of anthropomorphic phantoms could be an excellent substitute. The aim of the study was to design and develop a patient-specific 3D-skull and brain model with realistic CT-attenuation suitable for conventional and augmented reality (AR)-navigated neurosurgical simulations. Methods: The radiodensity of materials considered for the skull and brain phantoms were investigated using cone beam CT (CBCT) and compared to the radiodensities of the human skull and brain. The mechanical properties of the materials considered were tested in the laboratory and subsequently evaluated by clinically active neurosurgeons. Optimization of the phantom for the intended purposes was performed in a feedback cycle of tests and improvements. Results: The skull, including a complete representation of the nasal cavity and skull base, was 3D printed using polylactic acid with calcium carbonate. The brain was cast using a mixture of water and coolant, with 4 wt% polyvinyl alcohol and 0.1 wt% barium sulfate, in a mold obtained from segmentation of CBCT and T1 weighted MR images from a cadaver. The experiments revealed that the radiodensities of the skull and brain phantoms were 547 and 38 Hounsfield units (HU), as compared to real skull bone and brain tissues with values of around 1300 and 30 HU, respectively. As for the mechanical properties testing, the brain phantom exhibited a similar elasticity to real brain tissue. The phantom was subsequently evaluated by neurosurgeons in simulations of endonasal skull-base surgery, brain biopsies, and external ventricular drain (EVD) placement and found to fulfill the requirements of a surgical phantom. Conclusions: A realistic and CT-compatible anthropomorphic head phantom was designed and successfully used for simulated augmented reality-led neurosurgical procedures. The anatomic details of the skull base and brain were realistically reproduced. This phantom can easily be manufactured and used for surgical training at a low cost.
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- Manni, F, et al.
(författare)
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Hyperspectral Imaging for Glioblastoma Surgery: Improving Tumor Identification Using a Deep Spectral-Spatial Approach
- 2020
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Ingår i: Sensors (Basel, Switzerland). - : MDPI AG. - 1424-8220. ; 20:23
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Tidskriftsartikel (refereegranskat)abstract
- The primary treatment for malignant brain tumors is surgical resection. While gross total resection improves the prognosis, a supratotal resection may result in neurological deficits. On the other hand, accurate intraoperative identification of the tumor boundaries may be very difficult, resulting in subtotal resections. Histological examination of biopsies can be used repeatedly to help achieve gross total resection but this is not practically feasible due to the turn-around time of the tissue analysis. Therefore, intraoperative techniques to recognize tissue types are investigated to expedite the clinical workflow for tumor resection and improve outcome by aiding in the identification and removal of the malignant lesion. Hyperspectral imaging (HSI) is an optical imaging technique with the power of extracting additional information from the imaged tissue. Because HSI images cannot be visually assessed by human observers, we instead exploit artificial intelligence techniques and leverage a Convolutional Neural Network (CNN) to investigate the potential of HSI in twelve in vivo specimens. The proposed framework consists of a 3D–2D hybrid CNN-based approach to create a joint extraction of spectral and spatial information from hyperspectral images. A comparison study was conducted exploiting a 2D CNN, a 1D DNN and two conventional classification methods (SVM, and the SVM classifier combined with the 3D–2D hybrid CNN) to validate the proposed network. An overall accuracy of 80% was found when tumor, healthy tissue and blood vessels were classified, clearly outperforming the state-of-the-art approaches. These results can serve as a basis for brain tumor classification using HSI, and may open future avenues for image-guided neurosurgical applications.
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- Manni, F, et al.
(författare)
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Hyperspectral Imaging for Skin Feature Detection: Advances in Markerless Tracking for Spine Surgery
- 2020
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Ingår i: APPLIED SCIENCES-BASEL. - : MDPI AG. - 2076-3417. ; 10:12
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- In spinal surgery, surgical navigation is an essential tool for safe intervention, including the placement of pedicle screws without injury to nerves and blood vessels. Commercially available systems typically rely on the tracking of a dynamic reference frame attached to the spine of the patient. However, the reference frame can be dislodged or obscured during the surgical procedure, resulting in loss of navigation. Hyperspectral imaging (HSI) captures a large number of spectral information bands across the electromagnetic spectrum, providing image information unseen by the human eye. We aim to exploit HSI to detect skin features in a novel methodology to track patient position in navigated spinal surgery. In our approach, we adopt two local feature detection methods, namely a conventional handcrafted local feature and a deep learning-based feature detection method, which are compared to estimate the feature displacement between different frames due to motion. To demonstrate the ability of the system in tracking skin features, we acquire hyperspectral images of the skin of 17 healthy volunteers. Deep-learned skin features are detected and localized with an average error of only 0.25 mm, outperforming the handcrafted local features with respect to the ground truth based on the use of optical markers.
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