The use of pedicle screws for spinal stabilization has become a common technique in spine surgery. Due to the proximity of the inserted implant to the spinal column, high accuracy is required. In the current surgical routine, the screw trajectory is planned manually on a CT scan, which is prone to error and time-consuming. We propose an automatic pedicle screw placement approach that can simplify and speed up the screw planning suitable for intra-operative use. For automatic screw positioning in the CT volume, we designed a vertebra instance patch-based approach, employing a state-of-the-art U-Net framework, which estimates a mask for the screw pair location. From the predicted screw masks, we derive the desired screw parameters. The method was trained in a 5-fold cross-validation for the lumbar spine on a large training set of 155 patients (1052 screws) and evaluated on an external test set with 30 patients (198 screws). The automatically obtained and manually defined plans for screw trajectories showed a relatively high agreement and were clinically accepted by a spinal neurosurgeon based on the Gertzbein-Robbins classification. The mean absolute difference (MAD) and corresponding standard deviation (SD) was 0.4 ± 0.3 mm for the screw diameter, 4.6 ± 3.1 mm for the screw length, 4.3 ± 2.1 mm and 4.2 ± 2.4 mm for the head and axis point and 6.0 ± 3.7° for screw insertion directions with an average inference time per vertebra of 2.9 sec. For patch initialization, we propose a manual and automatic vertebra center localization technique, facilitating the seamless integration of the method into the clinical workflow.
KEYWORDS: Multispectral imaging, Monte Carlo methods, Tissue optics, Surgery, Tumors, Blood, In vivo imaging, Laparoscopy, Machine learning, Image resolution
Multispectral imaging (MSI) could be useful for many applications in surgery, including tumor detection and perfusion monitoring. Acquisition of many bands however leads to long imaging times and/or low resolution, hampering widespread adoption of the technique. To overcome this issue, current research focusses on reducing the number of recorded bands. Yet, the methods proposed are not able to consider both the target domain (e.g. liver surgery) and the specific task (e.g. oxygenation or blood volume fraction monitoring) when selecting bands.
In this work we present the first approach to domain and task specific band selection. Our method relies on highly generic Monte Carlo-based tissue simulations that aim to capture a large range of optical tissue parameters potentially observed during surgical interventions. The adaptation of the model to a specific clinical application is based on label-free in vivo hyperspectral recordings using a recently published approach to multispectral domain adaptation. The bands are selected based on their performance to estimate a task-dependent physiological parameter. This performance is evaluated on the adapted simulations, which come with ground truth values. According to in vivo experiments with hyperspectral recordings of tumors in a mouse model, a small subset of bands is enough for accurate oxygenation and blood volume fraction estimation. Compared to state-of-the-art baseline methods, bands selected by our method show more accurate results in oxygenation estimation. Our work could thus help remove one of the last barriers for interventional usage of MSI.
KEYWORDS: Sensors, Monte Carlo methods, Image processing, Photoacoustic spectroscopy, Reconstruction algorithms, Computer simulations, Error analysis, Data modeling, Tissues, Medical imaging
Quantification of tissue properties with photoacoustic (PA) imaging typically requires a highly accurate representation of the initial pressure distribution in tissue. Almost all PA scanners reconstruct the PA image only from a partial scan of the emitted sound waves. Especially handheld devices, which have become increasingly popular due to their versatility and ease of use, only provide limited view data because of their geometry. Owing to such limitations in hardware as well as to the acoustic attenuation in tissue, state-of-the-art reconstruction methods deliver only approximations of the initial pressure distribution. To overcome the limited view problem, we present a machine learning-based approach to the reconstruction of initial pressure from limited view PA data. Our method involves a fully convolutional deep neural network based on a U-Net-like architecture with pixel-wise regression loss on the acquired PA images. It is trained and validated on in silico data generated with Monte Carlo simulations. In an initial study we found an increase in accuracy over the state-of-the-art when reconstructing simulated linear-array scans of blood vessels.
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