Outer skull landmark-based coordinates for measurement of cerebral blood flow and intracranial pressure in rabbits

Despite the increased use of intracranial neuromonitoring during experimental subarachnoid hemorrhage (SAH), coordinates for probe placement in rabbits are lacking. This study evaluates the safety and reliability of using outer skull landmarks to identify locations for placement of cerebral blood flow (CBF) and intraparenchymal intracranial pressure (ICP) probes. Experimental SAH was performed in 17 rabbits using an extracranial-intracranial shunt model. ICP probes were placed in the frontal lobe and compared to measurements recorded from the olfactory bulb. CBF probes were placed in various locations in the frontal cortex anterior to the coronary suture. Insertion depth, relation to the ventricular system, and ideal placement location were determined by post-mortem examination. ICP recordings at the time of SAH from the frontal lobe did not differ significantly from those obtained from the right olfactory bulb. Ideal coordinates for intraparenchymal CBF probes in the left and right frontal lobe were found to be located 4.6±0.9 and 4.5±1.2 anterior to the bregma, 4.7±0.7mm and 4.7±0.5mm parasagittal, and at depths of 4±0.5mm and 3.9±0.5mm, respectively. The results demonstrate that the presented coordinates based on skull landmarks allow reliable placement of intraparenchymal ICP and CBF probes in rabbit brains without the use of a stereotactic frame.

The location of paravertebral catheters placed using the landmark technique

The aim of this prospective clinical study was to evaluate the location of paravertebral catheters that were placed using the classical landmark puncture technique and to correlate the distribution of contrast dye injected through the catheters with the extent of somatic block. Paravertebral catheter placement was attempted in 31 patients after video-assisted thoracic surgery. In one patient, an ultrasound-guided approach was chosen after failed catheter placement using the landmark method. A fluoroscopic examination in two planes using contrast dye was followed by injection of local anaesthetic and subsequent clinical testing of the extent of the anaesthetised area. In nine patients (29%), spread of contrast dye was not seen within the paravertebral space as intended. Misplaced catheters were in the epidural space (three patients), in the erector spinae musculature (five patients), and in the pleural space (one patient). There was also a discrepancy between the radiological findings and the observed distribution of loss of sensation. We have demonstrated an unacceptably high misplacement rate of paravertebral catheters using the landmark method. Additional research is required to compare the efficacy and safety of continuous paravertebral block using ultrasound-guided techniques or surgical inserted catheters.

Landmark-based augmented reality system for paranasal and transnasal endoscopic surgeries

BACKGROUND: In this paper we present a landmark-based augmented reality (AR) endoscope system for endoscopic paranasal and transnasal surgeries along with fast and automatic calibration and registration procedures for the endoscope. METHODS: Preoperatively the surgeon selects natural landmarks or can define new landmarks in CT volume. These landmarks are overlaid, after proper registration of preoperative CT to the patient, on the endoscopic video stream. The specified name of the landmark, along with selected colour and its distance from the endoscope tip, is also augmented. The endoscope optics are calibrated and registered by fast and automatic methods. Accuracy of the system is evaluated in a metallic grid and cadaver set-up. RESULTS: Root mean square (RMS) error of the system is 0.8 mm in a controlled laboratory set-up (metallic grid) and was 2.25 mm during cadaver studies. CONCLUSIONS: A novel landmark-based AR endoscope system is implemented and its accuracy is evaluated. Augmented landmarks will help the surgeon to orientate and navigate the surgical field. Studies prove the capability of the system for the proposed application. Further clinical studies are planned in near future.

Landmark Detection for Fusion of Fundus and MRI Towards a Patient-Specific Multi-Modal Eye Model

Ophthalmologists typically acquire different image modalities to diagnose eye pathologies. They comprise e.g., Fundus photography, Optical Coherence Tomography (OCT), Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Yet, these images are often complementary and do express the same pathologies in a different way. Some pathologies are only visible in a particular modality. Thus, it is beneficial for the ophthalmologist to have these modalities fused into a single patient-specific model. The presented article’s goal is a fusion of Fundus photography with segmented MRI volumes. This adds information to MRI which was not visible before like vessels and the macula. This article’s contributions include automatic detection of the optic disc, the fovea, the optic axis and an automatic segmentation of the vitreous humor of the eye.

Automatic X-ray landmark detection and shape segmentation via data-driven joint estimation of image displacements

In this paper, we propose a new method for fully-automatic landmark detection and shape segmentation in X-ray images. To detect landmarks, we estimate the displacements from some randomly sampled image patches to the (unknown) landmark positions, and then we integrate these predictions via a voting scheme. Our key contribution is a new algorithm for estimating these displacements. Different from other methods where each image patch independently predicts its displacement, we jointly estimate the displacements from all patches together in a data driven way, by considering not only the training data but also geometric constraints on the test image. The displacements estimation is formulated as a convex optimization problem that can be solved efficiently. Finally, we use the sparse shape composition model as the a priori information to regularize the landmark positions and thus generate the segmented shape contour. We validate our method on X-ray image datasets of three different anatomical structures: complete femur, proximal femur and pelvis. Experiments show that our method is accurate and robust in landmark detection, and, combined with the shape model, gives a better or comparable performance in shape segmentation compared to state-of-the art methods. Finally, a preliminary study using CT data shows the extensibility of our method to 3D data.

Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge

Cephalometric analysis is an essential clinical and research tool in orthodontics for the orthodontic analysis and treatment planning. This paper presents the evaluation of the methods submitted to the Automatic Cephalometric X-Ray Landmark Detection Challenge, held at the IEEE International Symposium on Biomedical Imaging 2014 with an on-site competition. The challenge was set to explore and compare automatic landmark detection methods in application to cephalometric X-ray images. Methods were evaluated on a common database including cephalograms of 300 patients aged six to 60 years, collected from the Dental Department, Tri-Service General Hospital, Taiwan, and manually marked anatomical landmarks as the ground truth data, generated by two experienced medical doctors. Quantitative evaluation was performed to compare the results of a representative selection of current methods submitted to the challenge. Experimental results show that three methods are able to achieve detection rates greater than 80% using the 4 mm precision range, but only one method achieves a detection rate greater than 70% using the 2 mm precision range, which is the acceptable precision range in clinical practice. The study provides insights into the performance of different landmark detection approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.