On the evaluation of segmentation editing tools

Robotic Assistance System for Cone-Beam Computed Tomography-Guided Percutaneous Needle Placement

Purpose

The study aimed to evaluate a new robotic assistance system (RAS) for needle placement in combination with a multi-axis C-arm angiography system for cone-beam computed tomography (CBCT) in a phantom setting.

Materials and Methods

The RAS consisted of a tool holder, dedicated planning software, and a mobile platform with a lightweight robotic arm to enable image-guided needle placement in conjunction with CBCT imaging. A CBCT scan of the phantom was performed to calibrate the robotic arm in the scan volume and to plan the different needle trajectories. The trajectory data were sent to the robot, which then positioned the tool holder along the trajectory. A 19G needle was then manually inserted into the phantom. During the control CBCT scan, the exact needle position was evaluated and any possible deviation from the target lesion measured.

Results

In total, 16 needle insertions targeting eight in- and out-of-plane sites were performed. Mean angular deviation from planned trajectory to actual needle trajectory was 1.12°. Mean deviation from target point and actual needle tip position was 2.74 mm, and mean deviation depth from the target lesion to the actual needle tip position was 2.14 mm. Mean time for needle placement was 361 s. Only differences in time required for needle placement between in- and out-of-plane trajectories (337 s vs. 380 s) were statistically significant (p = 0.0214).

Conclusion

Using this RAS for image-guided percutaneous needle placement with CBCT was precise and efficient in the phantom setting.

MRI based volumetric measurements of vestibular schwannomas in patients with neurofibromatosis type 2: comparison of three different software tools

Neurofibromatosis type 2 is a neurogenetic disorder with an incidence of about 1:33.000. Hallmarks are bilateral benign vestibular schwannomas, which can lead to deafness or brainstem compression. Volumetric tumor measurements are essential to assess the efficacy of new therapies. We present a statistical and methodical comparison of three volumetric image analysis tools. We performed volumetric measurements on phantoms with predefined volumes (0.1 to 8.0 ml) and tumors seen on 32 head MRI scans from eight NF2 patients with BrainLab, ITK-Snap, or OsiriX. The software was compared with regard to accuracy and reproducibility of the measurements and time required for analysis. The mean volume estimated by all three software programs differed significantly from the true volume of the phantoms, but OsiriX and BrainLab gave estimates that were not significantly different from each other. For the actual tumors, the estimated volumes with all three software tools showed a low coefficient of variability, but the mean volume estimates differed among the tools. OsiriX showed the shortest analysis time. Volumetric assessment of MRI images is associated to an intrinsic risk of miscalculation. For precise volumes it is mandatory to use the same volumetric tools for all measurements.

FISICO: Fast Image SegmentatIon COrrection

Background and Purpose

In clinical diagnosis, medical image segmentation plays a key role in the analysis of pathological regions. Despite advances in automatic and semi-automatic segmentation techniques, time-effective correction tools are commonly needed to improve segmentation results. Therefore, these tools must provide faster corrections with a lower number of interactions, and a user-independent solution to reduce the time frame between image acquisition and diagnosis.

Methods

We present a new interactive method for correcting image segmentations. Our method provides 3D shape corrections through 2D interactions. This approach enables an intuitive and natural corrections of 3D segmentation results. The developed method has been implemented into a software tool and has been evaluated for the task of lumbar muscle and knee joint segmentations from MR images.

Results

Experimental results show that full segmentation corrections could be performed within an average correction time of 5.5±3.3 minutes and an average of 56.5±33.1 user interactions, while maintaining the quality of the final segmentation result within an average Dice coefficient of 0.92±0.02 for both anatomies. In addition, for users with different levels of expertise, our method yields a correction time and number of interaction decrease from 38±19.2 minutes to 6.4±4.3 minutes, and 339±157.1 to 67.7±39.6 interactions, respectively.

Optimizing the 3D-reconstruction technique for serial block-face scanning electron microscopy

BACKGROUND

Elucidating the anatomy of neuronal circuits and localizing the synaptic connections between neurons, can give us important insights in how the neuronal circuits work. We are using serial block-face scanning electron microscopy (SBEM) to investigate the anatomy of a collision detection circuit including the Lobula Giant Movement Detector (LGMD) neuron in the locust, Locusta migratoria. For this, thousands of serial electron micrographs are produced that allow us to trace the neuronal branching pattern.

NEW METHOD

The reconstruction of neurons was previously done manually by drawing cell outlines of each cell in each image separately. This approach was very time consuming and troublesome. To make the process more efficient a new interactive software was developed. It uses the contrast between the neuron under investigation and its surrounding for semi-automatic segmentation.

RESULTS

For segmentation the user sets starting regions manually and the algorithm automatically selects a volume within the neuron until the edges corresponding to the neuronal outline are reached. Internally the algorithm optimizes a 3D active contour segmentation model formulated as a cost function taking the SEM image edges into account. This reduced the reconstruction time, while staying close to the manual reference segmentation result.

COMPARISON WITH EXISTING METHODS

Our algorithm is easy to use for a fast segmentation process, unlike previous methods it does not require image training nor an extended computing capacity.

CONCLUSION

Our semi-automatic segmentation algorithm led to a dramatic reduction in processing time for the 3D-reconstruction of identified neurons.