Image-Aided LiDAR Mapping Platform and Data Processing Strategy for Stockpile Volume Estimation

Stockpile quantity monitoring is vital for agencies and businesses to maintain inventory of bulk material such as salt, sand, aggregate, lime, and many other materials commonly used in agriculture, highways, and industrial applications. Traditional approaches for volumetric assessment of bulk material stockpiles, e.g., truckload counting, are inaccurate and prone to cumulative errors over long time. Modern aerial and terrestrial remote sensing platforms equipped with camera and/or light detection and ranging (LiDAR) units have been increasingly popular for conducting high-fidelity geometric measurements. Current use of these sensing technologies for stockpile volume estimation is impacted by environmental conditions such as lack of global navigation satellite system (GNSS) signals, poor lighting, and/or featureless surfaces. This study addresses these limitations through a new mapping platform denoted as Stockpile Monitoring and Reporting Technology (SMART), which is designed and integrated as a time-efficient, cost-effective stockpile monitoring solution. The novel mapping framework is realized through camera and LiDAR data-fusion that facilitates stockpile volume estimation in challenging environmental conditions. LiDAR point clouds are derived through a sequence of data collections from different scans. In order to handle the sparse nature of the collected data at a given scan, an automated image-aided LiDAR coarse registration technique is developed followed by a new segmentation approach to derive features, which are used for fine registration. The resulting 3D point cloud is subsequently used for accurate volume estimation. Field surveys were conducted on stockpiles of varying size and shape complexity. Independent assessment of stockpile volume using terrestrial laser scanners (TLS) shows that the developed framework had close to 1% relative error.

Volumetric assessment and longitudinal changes of brain structures in formalinized Beagle brains

High field MRI represents an advanced technique both for diagnostic and research purposes on animal models such as the Beagle dog. The increasing interest in non-invasive neuroscience, aging, and neuropathological research led to a need of reference values (in terms of volumetric assessment) for the typical brain structures involved and, nowadays, several canine brain MRI atlases have been provided. Since no reports are available regarding the measurements reproducibility and few information are available about formalin fixation effect on brain structures when applied to MRI segmentation, we assessed the segmentation variability of selected structures as a function of the operator (two operators segmented the same data) and their intrinsic variability within a sample of 11 Beagle dogs (9 females and 2 males, 1.6 ± 0.2 years). Then, we analyzed for one further Beagle dog (2 years old) the longitudinal changes in the brain segmentations of these structures corresponding four conditions: in vivo, post mortem (after euthanasia), ex vivo (brain extracted and studied after 1 month in formalin and after 11 months); all the MRI images were collected with a 3 T MRI scanner. Our findings suggest that the segmentation procedure can be considered overall reproducible since only slight statistical differences were detected, apart from the ventricles.Furthermore, in the post mortem/ ex vivo comparison, the majority of the structures showed a higher contrast leading to more reproducible segmentations across operators and a net increase of volume of the studied structures; this could be justified by the intrinsic relaxation time changes observed as a consequence of formalin fixation, that led to an improvement of brain structures visualization and then segmentation.To conclude, MRI based segmentation seems to be a useful and accurate tool that allows longitudinal studies, especially when applied to formalin fixed brains.

Three-dimensional semi-automated volumetric assessment of the pulp space of teeth following regenerative dental procedures

The volumetric change that occurs in the pulp space over time represents a critical measure when it comes to determining the secondary outcomes of regenerative endodontic procedures (REPs). However, to date, only a few studies have investigated the accuracy of the available domain-specialized medical imaging tools with regard to three-dimensional (3D) volumetric assessment. This study sought to compare the accuracy of two different artificial intelligence-based medical imaging programs namely OsiriX MD (v 9.0, Pixmeo SARL, Bernex Switzerland, https://www.osirix-viewer.com) and 3D Slicer (http://www.slicer.org), in terms of estimating the volume of the pulp space following a REP. An Invitro assessment was performed to check the reliability and sensitivity of the two medical imaging programs in use. For the subsequent clinical application, pre- and post-procedure cone beam computed tomography scans of 35 immature permanent teeth with necrotic pulp and periradicular pathosis that had been treated with a cell-homing concept-based REP were processed using the two biomedical DICOM software programs (OsiriX MD and 3D Slicer). The volumetric changes in the teeth’s pulp spaces were assessed using semi-automated techniques in both programs. The data were statistically analyzed using t-tests and paired t-tests (P = 0.05). The pulp space volumes measured using both programs revealed a statistically significant decrease in the pulp space volume following the REP (P < 0.05), with no significant difference being found between the two programs (P > 0.05). The mean decreases in the pulp space volumes measured using OsiriX MD and 3D Slicer were 25.06% ± 19.45% and 26.10% ± 18.90%, respectively. The open-source software (3D Slicer) was found to be as accurate as the commercially available software with regard to the volumetric assessment of the post-REP pulp space. This study was the first to demonstrate the step-by-step application of 3D Slicer, a user-friendly and easily accessible open-source multiplatform software program for the segmentation and volume estimation of the pulp spaces of teeth treated with REPs.

3D-printed phantoms to quantify accuracy and variability of goniometric and volumetric assessment of Peyronie’s disease deformities

Characterization of Peyronie’s disease (PD) involves manual goniometry and penile length measurement. These techniques neglect volume loss or hourglass deformities. Inter-provider variability complicates accuracy. Using 3D-printed models, we aimed to evaluate measurement accuracy and variability and establish computational assessment workflows. Five digital phantoms were created: 13.0 cm cylinder, 13.0 cm hourglass cylinder, 15.0 cm cylinder with 40° angulation, 12.0 cm straight penis, and 12.9 cm PD penis with 68° angulation and hourglass. Lengths, volumes, and angles were determined computationally. Each phantom was 3D-printed. Ten urology providers determined lengths, angles, and volumes with measuring tape, goniometer, and volume calculator. Provider versus computational measurements were compared to determine accuracy using t-tests or Wilcoxon rank-sum tests. No significant differences were observed between manual assessment of length of penile models and designed length in penile models. Average curvature angles from providers for bent cylinder and PD phantoms were 38.3° ± 3.9° (p = 0.25) and 57.5° ± 7.2° (p = 0.006), respectively. When assessing for volume, hourglass cylinder and bent cylinder showed significant differences between designed volume and provider averages. All assessments of length, angle, and volume showed significant provider variability. Our results suggest manual measurements suffer from inaccuracy and variability. Computational workflows are useful for improved accuracy and volume assessment.

TWMP-01. RESECTION OF SUPPLEMENTARY MOTOR AREA GLIOMAS: REVISITING SUPPLEMENTARY MOTOR SYNDROME AND THE ROLE OF THE FRONTAL ASLANT TRACT

INTRODUCTION The supplemental motor area (SMA) is an eloquent region that is frequently a site for gliomas or the region is included in the resection trajectory to deeper lesions. METHODS Patient, tumor and outcome data were collected retrospectively from the UCSF tumor registry for patients who underwent surgical resection for newly diagnosed supratentorial diffuse glioma (WHO Grade II - IV) between 2010 and 2019 in the SMA region and the extent of SMA resection was determined by volumetric assessment. Tumors were registered to a standard brain atlas to create a frequency heat map of tumor volumes and resection cavities. RESULTS Although the volume of tumor within the SMA region did not correlate with the development of SMA syndrome, patients with SMA syndrome had larger resection cavities in the SMA region (25.4% SMA resection vs. 14.2% SMA resection, p = 0.039). The size of the resection cavity in the SMA region did not correlate with the severity of the SMA syndrome. Patients who developed SMA syndrome had cavities that were located more posteriorly in the SMA region and in the cingulate. When the frontal aslant tract (FAT) was preserved, 50% of patients developed SMA syndrome post-operatively; whereas 100% of patients who had disruption of the FAT during surgery developed SMA syndrome (p = 0.06). There was no difference in the overall survival for newly diagnosed glioblastoma patients with SMA syndrome compared to those without SMA syndrome (1.6 years vs. 3.0 years, p = 0.33). CONCLUSION For patients with SMA gliomas, larger resections and resections involving the posterior SMA region and posterior cingulate gyrus increased the likelihood of a post-operative SMA syndrome. Although SMA syndrome occurred in all cases where the FAT was resected, FAT preservation does not reliably avoid SMA syndrome post-operatively.

EASE: Clinical Implementation of Automated Tumor Segmentation and Volume Quantification for Adult Low-Grade Glioma

The growth rate of non-enhancing low-grade glioma has prognostic value for both malignant progression and survival, but quantification of growth is difficult due to the irregular shape of the tumor. Volumetric assessment could provide a reliable quantification of tumor growth, but is only feasible if fully automated. Recent advances in automated tumor segmentation have made such a volume quantification possible, and this work describes the clinical implementation of automated volume quantification in an application named EASE: Erasmus Automated SEgmentation. The visual quality control of segmentations by the radiologist is an important step in this process, as errors in the segmentation are still possible. Additionally, to ensure patient safety and quality of care, protocols were established for the usage of volume measurements in clinical diagnosis and for future updates to the algorithm. Upon the introduction of EASE into clinical practice, we evaluated the individual segmentation success rate and impact on diagnosis. In its first 3 months of usage, it was applied to a total of 55 patients, and in 36 of those the radiologist was able to make a volume-based diagnosis using three successful consecutive measurements from EASE. In all cases the volume-based diagnosis was in line with the conventional visual diagnosis. This first cautious introduction of EASE in our clinic is a valuable step in the translation of automatic segmentation methods to clinical practice.