Semi-automatic Image Segmentation Research Articles

Quantitative Analysis of Whole-Mount Fluorescence-Stained Tumor Spheroids in Phenotypic Drug Screens.

Three-dimensional cell cultures, such as spheroids or organoids, serve as important models for drug screening purposes. Optical tissue clearing (OTC) enhances the visualization of fluorescence stainings and enables in toto microscopy of 3D cell culture models. Furthermore, subsequent automated image analysis tools convert qualitative confocal image sets into quantitative data. In this chapter, we describe a detailed protocol for preparation of HT29 cancer spheroids, 3D in toto immunostaining, glycerol-based OTC, whole-mount imaging, and semi-automated downstream image processing and segmentation for nuclear image analysis using open-source software.

A Segmentation Method of Serialized Human Body Slices Based on Matting Strategy and Skeleton Extraction.

In this paper, a semiautomatic image segmentation method for the serialized body slices of the Visible Human Project (VHP) is proposed. In our method, we first verified the effectiveness of the shared matting method for the VHP slices and utilized it to segment a single image. Then, to meet the need for the automatic segmentation of serialized slice images, a method based on the parallel refinement method and flood-fill method was designed. The ROI (region of interest) image of the next slice can be extracted by using the skeleton image of the ROI in the current slice. Utilizing this strategy, the color slice images of the Visible Human body can be continuously and serially segmented. This method is not complex but is rapid and automatic with less manual participation. The experimental results show that the primary organs of the Visible Human body can be accurately extracted.

Predicting vasospasm risk using first presentation aneurysmal subarachnoid hemorrhage volume: A semi-automated CT image segmentation analysis using ITK-SNAP.

Cerebral vasospasm following aneurysmal subarachnoid hemorrhage (aSAH) is a significant complication associated with poor neurological outcomes. We present a novel, semi-automated pipeline, implemented in the open-source medical imaging analysis software ITK-SNAP, to segment subarachnoid blood volume from initial CT head (CTH) scans and use this to predict future radiological vasospasm. 42 patients were admitted between February 2020 and December 2021 to our tertiary neurosciences center, and whose initial referral CTH scan was used for this retrospective cohort study. Blood load was segmented using a semi-automated random forest classifier and active contour evolution implemented in ITK-SNAP. Clinical data were extracted from electronic healthcare records in order to fit models aimed at predicting radiological vasospasm risk. Semi-automated segmentations demonstrated excellent agreement with manual, expert-derived volumes (mean Dice coefficient = 0.92). Total normalized blood volume, extracted from CTH images at first presentation, was significantly associated with greater odds of later radiological vasospasm, increasing by approximately 7% for each additional cm3 of blood (OR = 1.069, 95% CI: 1.021-1.120; p < .005). Greater blood volume was also significantly associated with vasospasm of a higher Lindegaard ratio, of longer duration, and a greater number of discrete episodes. Total blood volume predicted radiological vasospasm with a greater accuracy as compared to the modified Fisher scale (AUC = 0.86 vs 0.70), and was of independent predictive value. Semi-automated methods provide a plausible pipeline for the segmentation of blood from CT head images in aSAH, and total blood volume is a robust, extendable predictor of radiological vasospasm, outperforming the modified Fisher scale. Greater subarachnoid blood volume significantly increases the odds of subsequent vasospasm, its time course and its severity.

Deep learning of image-derived measures of body composition in pediatric, adolescent, and young adult lymphoma: association with late treatment effects.

The objective of this study was to translate a deep learning (DL) approach for semiautomated analysis of body composition (BC) measures from standard of care CT images to investigate the prognostic value of BC in pediatric, adolescent, and young adult (AYA) patients with lymphoma. This 10-year retrospective, single-site study of 110 pediatric and AYA patients with lymphoma involved manual segmentation of fat and muscle tissue from 260 CT imaging datasets obtained as part of routine imaging at initial staging and first therapeutic follow-up. A DL model was trained to perform semiautomated image segmentation of adipose and muscle tissue. The association between BC measures and the occurrence of 3-year late effects was evaluated using Cox proportional hazards regression analyses. DL-guided measures of BC were in close agreement with those obtained by a human rater, as demonstrated by high Dice scores (≥ 0.95) and correlations (r > 0.99) for each tissue of interest. Cox proportional hazards regression analyses revealed that patients with elevated subcutaneous adipose tissue at baseline and first follow-up, along with patients who possessed lower volumes of skeletal muscle at first follow-up, have increased risk of late effects compared to their peers. DL provides rapid and accurate quantification of image-derived measures of BC that are associated with risk for treatment-related late effects in pediatric and AYA patients with lymphoma. Image-based monitoring of BC measures may enhance future opportunities for personalized medicine for children with lymphoma by identifying patients at the highest risk for late effects of treatment. • Deep learning-guided CT image analysis of body composition measures achieved high agreement level with manual image analysis. • Pediatric patients with more fat and less muscle during the course of cancer treatment were more likely to experience a serious adverse event compared to their clinical counterparts. • Deep learning of body composition may add value to routine CT imaging by offering real-time monitoring of pediatric, adolescent, and young adults at high risk for late effects of cancer treatment.

Optical coherence tomography for in vivo longitudinal monitoring of artificial dermal scaffold.

Artificial dermal scaffold (ADS) has undergone rapid development and been increasingly used for treating skin wound in clinics due to its good biocompatibility, controllable degradation, and low risk of disease infection. To obtain good treatment efficacy, ADS needs to be monitored longitudinally during the treatment process. For example, scaffold-tissue fit, cell in-growth, vascular regeneration, and scaffold degradation are the key properties to be inspected. However, to date, there are no effective, real-time, and noninvasive techniques to meet the requirement of the scaffold monitoring above. In this study, we propose to use optical coherence tomography (OCT) to monitor ADS in vivo through three-dimensional imaging. A swept source OCT system with a handheld probe was developed for in vivo skin imaging. Moreover, a cell in-growth, vascular regeneration, and scaffold degradation rate (IRDR) was defined with the volume reduction rate of the scaffold's collagen sponge layer. To measure the IRDR, a semiautomatic image segmentation algorithm was designed based on U-Net to segment the collagen sponge layer of the scaffold from OCT images. The results show that the scaffold-tissue fit can be clearly visualized under OCT imaging. The IRDR can be computed based on the volume of the segmented collagen sponge layer. It is observed that the IRDR appeared to a linear function of the time and in addition, the IRDR varied among different skin parts. Overall, it can be concluded that OCT has a good potential to monitor ADS in vivo. This can help guide the clinicians to control the treatment with ADS to improve the therapy.

Accuracy and processing time of kidney volume measurement methods in rodents polycystic kidney disease models: superiority of semiautomated kidney segmentation.

Measurement of total kidney volume (TKV) using magnetic resonance imaging (MRI) is a valuable approach for monitoring disease progression in autosomal dominant polycystic kidney disease (PKD) and is becoming more common in preclinical studies using animal models. Manual contouring of kidney MRI areas [i.e., manual method (MM)] is a conventional, but time-consuming, way to determine TKV. We developed a template-based semiautomatic image segmentation method (SAM) and validated it in three commonly used PKD models: Cys1cpk/cpk mice, Pkd1RC/RC mice, and Pkhd1pck/pck rats (n = 10 per model). We compared SAM-based TKV with that obtained by clinical alternatives including the ellipsoid formula-based method (EM) using three kidney dimensions, the longest kidney length method (LM), and MM, which is considered the gold standard. Both SAM and EM presented high accuracy in TKV assessment in Cys1cpk/cpk mice [interclass correlation coefficient (ICC) ≥ 0.94]. SAM was superior to EM and LM in Pkd1RC/RC mice (ICC = 0.87, 0.74, and <0.10 for SAM, EM, and LM, respectively) and Pkhd1pck/pck rats (ICC = 0.59, <0.10, and <0.10, respectively). Also, SAM outperformed EM in processing time in Cys1cpk/cpk mice (3.6 ± 0.6 vs. 4.4 ± 0.7 min/kidney) and Pkd1RC/RC mice (3.1 ± 0.4 vs. 7.1 ± 2.6 min/kidney, both P < 0.001) but not in Pkhd1PCK/PCK rats (3.7 ± 0.8 vs. 3.2 ± 0.5 min/kidney). LM was the fastest (∼1 min) but correlated most poorly with MM-based TKV in all studied models. Processing times by MM were longer for Cys1cpk/cpk mice, Pkd1RC/RC mice, and Pkhd1pck.pck rats (66.1 ± 7.3, 38.3 ± 7.5, and 29.2 ± 3.5 min). In summary, SAM is a fast and accurate method to determine TKV in mouse and rat PKD models.NEW & NOTEWORTHY Total kidney volume (TKV) is a valuable readout in preclinical studies for autosomal dominant and autosomal recessive polycystic kidney diseases (ADPKD and ARPKD). Since conventional TKV assessment by manual contouring of kidney areas in all images is time-consuming, we developed a template-based semiautomatic image segmentation method (SAM) and validated it in three commonly used ADPKD and ARPKD models. SAM-based TKV measurements were fast, highly reproducible, and accurate across mouse and rat ARPKD and ADPKD models.

An Enhanced Pignistic Transformation-Based Fusion Scheme With Applications in Image Segmentation

The traditional Pignistic transformation is limited in the context of “betting”, which faces information loss and is inconvenient for multi-source information fusion. To tackle this challenge, an Enhanced Pignistic transformation is proposed for the first time. New divergence and information volume measures are tailor-made for the enhanced Pignistic probability, and a novel information fusion algorithm is developed. To further prove the fusion algorithm’s advantages in conflict management, it is applied in a new semi-automatic image segmentation scheme. Two uncertain decision-support techniques named adaptive belief assignment and scalable information extraction are raised, and a fuzzy heuristic refinement algorithm is conducted, fulfilling the gap between evidential decision-making and segmentation refinement. Experimental analysis shows the proposed segmentation algorithm is superior on four metrics each and can enhance the robustness of foreground segmentation, indicating the effectiveness of the proposal in solving the decision inaccuracy of evidential segmentation schemes.

A fast and reliable method for semi-automated planimetric quantification of dental plaque in clinical trials.

To develop a simple and reproducible method for semi-automated planimetric quantification of dental plaque. Plaque from 20 healthy volunteers was disclosed using erythrosine, and fluorescence images of the first incisors, first premolars, and first molars were recorded after 1, 7, and 14 days of de novo plaque formation. The planimetric plaque index (PPI) was determined using a semi-automated threshold-based image segmentation algorithm and compared with manually determined PPI and the Turesky modification of the Quigley-Hein plaque index (TM-QHPI). The decrease of tooth autofluorescence in plaque-covered areas was quantified as an index of plaque thickness (TI). Data were analysed by analysis of variance (ANOVA) and Pearson correlations. The high contrast between teeth, disclosed plaque, and soft tissues in fluorescence images allowed for a fast threshold-based image segmentation. Semi-automated PPI is strongly correlated with manual planimetry (r=0.92; p < .001) and TM-QHPI recordings (r=0.88; p < .001), and may exhibit a higher discriminatory power than TM-QHPI due to its continuous scale. TI values corresponded to optically perceived plaque thickness, and no differences were observed over time (p > .05, ANOVA). The proposed semi-automated planimetric analysis based on fluorescence images is a simple and efficient method for dental plaque quantification in multiple images with reduced human input.

Influence of Image Processing on Radiomic Features From Magnetic Resonance Imaging.

Before implementing radiomics in routine clinical practice, comprehensive knowledge about the repeatability and reproducibility of radiomic features is required. The aim of this study was to systematically investigate the influence of image processing parameters on radiomic features from magnetic resonance imaging (MRI) in terms of feature values as well as test-retest repeatability. Utilizing a phantom consisting of 4 onions, 4 limes, 4 kiwifruits, and 4 apples, we acquired a test-retest dataset featuring 3 of the most commonly used MRI sequences on a 3 T scanner, namely, a T1-weighted, a T2-weighted, and a fluid-attenuated inversion recovery sequence, each at high and low resolution. After semiautomatic image segmentation, image processing with systematic variation of image processing parameters was performed, including spatial resampling, intensity discretization, and intensity rescaling. For each respective image processing setting, a total of 45 radiomic features were extracted, corresponding to the following 7 matrices/feature classes: conventional indices, histogram matrix, shape matrix, gray-level zone length matrix, gray-level run length matrix, neighboring gray-level dependence matrix, and gray-level cooccurrence matrix. Systematic differences of individual features between different resampling steps were assessed using 1-way analysis of variance with Tukey-type post hoc comparisons to adjust for multiple testing. Test-retest repeatability of radiomic features was measured using the concordance correlation coefficient, dynamic range, and intraclass correlation coefficient. Image processing influenced radiological feature values. Regardless of the acquired sequence and feature class, significant differences ( P < 0.05) in feature values were found when the size of the resampled voxels was too large, that is, bigger than 3 mm. Almost all higher-order features depended strongly on intensity discretization. The effects of intensity rescaling were negligible except for some features derived from T1-weighted sequences. For all sequences, the percentage of repeatable features (concordance correlation coefficient and dynamic range ≥ 0.9) varied considerably depending on the image processing settings. The optimal image processing setting to achieve the highest percentage of stable features varied per sequence. Irrespective of image processing, the fluid-attenuated inversion recovery sequence in high-resolution overall yielded the highest number of stable features in comparison with the other sequences (89% vs 64%-78% for the respective optimal image processing settings). Across all sequences, the most repeatable features were generally obtained for a spatial resampling close to the originally acquired voxel size and an intensity discretization to at least 32 bins. Variation of image processing parameters has a significant impact on the values of radiomic features as well as their repeatability. Furthermore, the optimal image processing parameters differ for each MRI sequence. Therefore, it is recommended that these processing parameters be determined in corresponding test-retest scans before clinical application. Extensive repeatability, reproducibility, and validation studies as well as standardization are required before quantitative image analysis and radiomics can be reliably translated into routine clinical care.

Performance evaluation of segmentation methods for assessing the lens of the frog Thoropa miliaris from synchrotron-based phase-contrast micro-CT images.

In the context of synchrotron microtomography using propagation-based phase-contrast imaging (XSPCT), we evaluated the performance of semiautomatic and automatic image segmentation of soft biological structures by means of Dice Similarity Coefficient (DSC) and volume quantification. We took advantage of the phase-contrast effects of XSPCT to provide enhanced object boundaries and improved visualization of the lenses of the frog Thoropa miliaris. Then, we applied semiautomatic segmentation methods 1 and 2 (Interpolation and Watershed, respectively) and method 3, an automatic segmentation algorithm using the U-Net architecture, to the reconstructed images. DSC and volume quantification of the lenses were used to quantify the performance of image segmentation methods. Comparing the lenses segmented by the three methods, the most pronounced difference in volume quantification was between methods 1 and 3: a reduction of 4.24%. Method 1, 2 and 3 obtained the global average DSC of 97.02%, 95.41% and 89.29%, respectively. Although it obtained the lowest DSC, method 3 performed the segmentation in a matter of seconds, while the semiautomatic methods had the average time to segment the lenses around 1h and 30min. Our results suggest that the performance of U-Net was impaired due to the irregularities of the ROI edges mainly in its lower and upper regions, but it still showed high accuracy (DSC=89.29%) with significantly reduced segmentation time compared to the semiautomatic methods. Besides, with the present work we have established a baseline for future assessments of Deep Neural Networks applied to XSPCT volumes.

MiTiSegmenter: Software for high throughput segmentation and meshing of microCT data in microtiter plate arrays

Lab-based microCT is a powerful means of visualising the internal structure of physical specimens deployed across the physical sciences, engineering and the arts. As its popularity has grown, demand for bulk digitisation of multiple samples within a single scan has increased. High throughput workflows can increase sample sizes and reduce scan time, yet downstream segmentation and meshing remain a bottleneck. We present MiTiSegmenter as a new tool for the bulk archiving of valuable zooarchaeological and palaeontological remains. We foresee MiTiSegmenter as particularly useful when incorporated into workflows that ultimately require the destructive testing of specimens, including sampling for ancient DNA and proteomics. The software may also play an important role in national museums' ongoing mass digitisation efforts, facilitating the high-speed archiving of specimen 3D morphology across extensive collections with very minimal user intervention or prior training. •We present MiTiSegmenter, a software package for semi-automated image processing and segmentation of array-based batch microCT data.•Implemented in Python, MiTiSegmenter expedites cropping, meshing and exporting samples within stacked microtiter plates, facilitating the rapid digitisation of hundreds-thousands of samples per scan.•We illustrate MiTiSegmenter's capabilities when applied to bulk archiving of valuable zooarchaeological and palaeontological remains

Hierarchical Random Walker Segmentation for Large Volumetric Biomedical Images.

The random walker method for image segmentation is a popular tool for semi-automatic image segmentation, especially in the biomedical field. However, its linear asymptotic run time and memory requirements make application to 3D datasets of increasing sizes impractical. We propose a hierarchical framework that, to the best of our knowledge, is the first attempt to overcome these restrictions for the random walker algorithm and achieves sublinear run time and constant memory complexity. The goal of this framework is- rather than improving the segmentation quality compared to the baseline method- to make interactive segmentation on out-of-core datasets possible. The method is evaluated quantitatively on synthetic data and the CT-ORG dataset where the expected improvements in algorithm run time while maintaining high segmentation quality are confirmed. The incremental (i.e., interaction update) run time is demonstrated to be in seconds on a standard PC even for volumes of hundreds of gigabytes in size. In a small case study the applicability to large real world from current biomedical research is demonstrated. An implementation of the presented method is publicly available in version 5.2 of the widely used volume rendering and processing software Voreen (https://www.uni-muenster.de/Voreen/).

Semi-Automatic Segmentation of Cone Beam Computed Tomography Datasets for Volume Measurements of Equine Cheek Teeth.

The volumes of equine teeth may change considerably over time for several reasons including domestication, routine dental floating, and the hypsodont and anelodont nature of the teeth. Cone beam computed tomography (CBCT) of the head is routinely performed in standing horses and, in this proof of concept study, the feasibility of measuring tooth volume from CBCT datasets was determined. The CBCT images of 5 equine cadaver cheek teeth were segmented with a software 3-dimensional (3D) Slicer using a predefined protocol, corrected manually, and re-assembled into a 3D model. Individual tooth volume (VS) was calculated from the model. After extraction, the volumes were also measured using the "gold-standard" water displacement method (VW) for comparison. The VS of 77 teeth ranged from 7114 to 42,300 mm3 which strongly correlated with VW (r = 0.99), and on average VS was 6.1% less than VW. There was no significant difference in VS between the right and left arcades in individual animals. Maxillary cheek tooth volume was on average 40% larger than it was for mandibular counterparts. Semi-automatic image segmentation of equine cheek teeth from CBCT data is feasible and accurate but requires some manual intervention. This preliminary study provides initial data on the volume of equine cheek teeth and creates new possibilities for future in vivo studies.

A Semiautomatic Multi-Label Color Image Segmentation Coupling Dirichlet Problem and Colour Distances.

Image segmentation is an essential but critical component in low level vision, image analysis, pattern recognition, and now in robotic systems. In addition, it is one of the most challenging tasks in image processing and determines the quality of the final results of the image analysis. Colour based segmentation could hence offer more significant extraction of information as compared to intensity or texture based segmentation. In this work, we propose a new local or global method for multi-label segmentation that combines a random walk based model with a direct label assignment computed using a suitable colour distance. Our approach is a semi-automatic image segmentation technique, since it requires user interaction for the initialisation of the segmentation process. The random walk part involves a combinatorial Dirichlet problem for a weighted graph, where the nodes are the pixel of the image, and the positive weights are related to the distances between pixels: in this work we propose a novel colour distance for computing such weights. In the random walker model we assign to each pixel of the image a probability quantifying the likelihood that the node belongs to some subregion. The computation of the colour distance is pursued by employing the coordinates in a colour space (e.g., RGB, XYZ, YCbCr) of a pixel and of the ones in its neighbourhood (e.g., in a 8–neighbourhood). The segmentation process is, therefore, reduced to an optimisation problem coupling the probabilities from the random walker approach, and the similarity with respect the labelled pixels. A further investigation involves an adaptive preprocess strategy using a regression tree for learning suitable weights to be used in the computation of the colour distance. We discuss the properties of the new method also by comparing with standard random walk and means approaches. The experimental results carried on the White Blood Cell (WBC) dataset and GrabCut datasets show the remarkable performance of the proposed method in comparison with state-of-the-art methods, such as normalised random walk and normalised lazy random walk, with respect to segmentation quality and computational time. Moreover, it reveals to be very robust with respect to the presence of noise and to the choice of the colourspace.

Semi-Automatic Image Segmentation on X-ray Image of Spine using Active Contour Method

Currently, many image analysis methods have been developed on X-Ray of scoliotic patients. However, segmentation of spinal curvature is still a challenge, and needs to be improved. In this research, we proposed a semi-automatic spinal image segmentation of scoliotic patients from X-Ray images. This method is divided into 2 steps: preprocessing and segmentation process. A conversion process from RGB to grayscale and CLAHE (Contrast Limited Adequate Histogram Equalization) method was used in image preprocessing. The active contour method was used for the segmentation process. The result shows that segmentation of spinal X-ray images of scoliotic patients using active contour method interactively, can give better results. The average of ME and RAE values are 12.98% and 26.75 %. instead of using the interactive region splitting method which gets 21.17% and 89.27%. Keywords: active contour, interactive segmentation, pre-processing, scoliosis.

Lower Ratio of Liver Volume and Body Weight Is a Negative Predictor of Survival after Transjugular Intrahepatic Portosystemic Shunt.

Transjugular intrahepatic portosystemic shunt (TIPS) is the most effective measure to treat complications of portal hypertension. However, liver function may deteriorate after TIPS. Predictors of liver function and outcome after TIPS are therefore important for management of TIPS patients. The study aimed to evaluate the impact of liver volume on transplant-free survival (TFS) after TIPS, as well as the evolution of liver volume and its relationship with liver function after TIPS. A retrospective analysis of all consecutive patients who underwent TIPS in a tertiary care university liver center between 2012 and 2017 (n = 216) was performed; n = 72 patients with complete prior and follow-up (FU) computed tomography (CT) imaging studies were included in the study. Volumetry of the liver was performed by a semi-automatic 9-lobe image segmentation algorithm at baseline and FU (FU 1: 90–180 d; FU 2: 180–365 d; FU 3: 365–545 d; FU 4: 545–730 d; FU 5: >730 d). Output variables were total liver volume (TLV, cm3), left liver volume (LLV, cm3), right liver volume (RLV, cm3) and TLV/body weight ratio. CT derived liver volumes were correlated with liver function tests, portosystemic pressure gradient (PPG) measurements and survival. To assess predictors of liver volume change over time we fitted linear mixed models. Kaplan–Meier analysis was performed and validated by matched pair analysis followed by Cox regression to determine independent prognostic factors for survival. The median TLV at baseline was 1507.5 cm3 (773.7–3686.0 cm3). Livers with higher baseline liver volumes and larger TLV/weight ratios retained their volume after an initial loss while smaller livers continuously lost volume after TIPS. At the first follow-up period (90–180 d post-TIPS) lower liver volumes and TLV/weight ratios were associated with higher bilirubin levels. Within the final multivariable model containing time (days since TIPS), baseline INR and baseline TLV, the average loss of liver volume was 0.74 mL per day after TIPS. Twelve-month overall transplant-free survival was 89% and median overall TFS was 33 months. The median TFS for a baseline TLV/body weight ratio > 20 was significantly higher compared with ≤20 (40.0 vs. 27.0 months, p = 0.010) while there were no differences regarding the indication for TIPS or etiology of liver disease in the matched pair analysis. Lower TLV/weight ratios before TIPS were associated with shorter TFS and should therefore be critically considered when selecting patients for TIPS. In addition, this study provides first evidence of an effect of TIPS on subsequent liver volume change and associated liver function.

Geomechanical and Fracture Network Interpretation of a Devonian Outcrop

Summary The Duvernay Formation is an unconventional reservoir characterized by induced seismicity and fluid migration, with natural fractures likely contributing to both cases. An alpine outcrop of the Perdrix and Flume formations, correlative with the subsurface Duvernay and Waterways formations, was investigated to characterize natural fracture networks. A semiautomated image-segmentation and fracture analysis was applied to orthomosaics generated from a photogrammetric survey to assess small- and large-scale fracture intensity and rock mass heterogeneity. The study also included manual scanlines, fracture windows, and Schmidt hammer measurements. The Perdrix section transitions from brittle fractures to en echelon fractures and shear-damage zones. Multiple scales of fractures were observed, including unconfined, bedbound fractures, and fold-related bed-parallel partings (BPPs). Variograms indicate a significant nugget effect along with fracture anisotropy. Schmidt hammer results lack correlation with fracture intensity. The Flume pavements exhibit a regionally extensive perpendicular joint set, tectonically driven fracturing, and multiple fault-damage zones with subvertical fractures dominating. Similar to the Perdrix, variograms show a significant nugget effect, highlighting fracture anisotropy. The results from this study suggest that small-scale fractures are inherently stochastic and that fractures observed at core scale should not be extrapolated to represent large-scale fracture systems; instead, the effects of small-scale fractures are best represented using an effective continuum approach. In contrast, large-scale fractures are more predictable according to structural setting and should be characterized robustly using geological principles. This study is especially applicable for operators and regulators in the Duvernay and similar formations where unconventional reservoir units abut carbonate formations.

P-186 Volumetric imaging provides insight into the 3D ultrastructural organization of maturing human oocytes

Abstract Study question Is volume electron microscopy suitable for high-resolution oocyte imaging in three dimensions (3D)? Summary answer Focused Ion Bean Scanning Electron Microscopy (FIB-SEM) allows 3D visualization and quantitative analysis of ultrastructural features in the large human oocyte volume. What is known already Transmission electron microscopy (TEM) has been traditionally used to study the fine morphology of female gametes. However, 2D micrographs provide only limited information about the topology of subcellular structures. Volumetric studies are needed to elucidate the 3D organization of complex oocyte cytoplasm. Study design, size, duration An academic study conducted in collaboration with an IVF clinic. Advanced 3D - ultrastructural analysis was performed on 9 human oocytes representing 3 stages of maturation (3 germinal vesicle (GV), 3 metaphase I (MI), 3 metaphase (MII) oocytes) collected from February 2018 to November 2019. Participants/materials, setting, methods Spare IVF oocytes, donated by 9 young egg donors (22-29 years), were cultured in vitro until they reached a defined developmental stage. Each oocytés meiotic status was determined based on the presence/absence of a prophase nucleus, a polar body, and a non-invasively detectable MI/MII spindle. Following standard TEM preparation, individual oocyte-containing resin blocks were coated with a thin carbon layer[JJ1] , mounted on the microscope stage, and subjected to FIB-SEM imaging. Main results and the role of chance FIB-SEM tomography provided an unprecedented view of the oocytés intracellular morphology. Automated serial scanning of newly exposed sample surface generated large stacks (120-1294 slices) of ultrastructural images with 40-100 nm z-resolution. The tomographic reconstruction of acquired datasets revealed the spatial arrangement of inner oocyteś structures. The imaging protocol was optimized to ensure sufficient image detail, minimal noisiness, and time-efficiency of large volume scanning. Comparison of oocytes fixed at different maturation stages confirmed previous TEM observations that the cortical region of GV oocytes is deprived of membraneous structures, and major organelle redistribution occurs during the MI phase. Semi-automated 3D image segmentation was employed to distinguish distinct organelle populations and evaluated their abundance. Subsequent quantitative analysis of volumetric data showed that the mitochondrion occupies ∼ 5.27 % of MII oocyte volume. In conclusion, the volumetric imaging, followed by advanced image analysis, maximizes the amount of morphological data obtained from a single human oocyte. Limitations, reasons for caution The imaging procedure was pioneered on a small number of hormonally-primed oocytes, which failed to complete development in vivo. There is a trade-off between resolution, the size of the 3D volume, and imaging time. Block-face ion milling during FIB-SEM imaging inevitably results in sample destruction. Wider implications of the findings This proof-of-concept study opens up new possibilities to study the delicate architecture of scarce human oocytes. Enhancing our knowledge of the spatial organization of ooplasm is pivotal for developing experimental and therapeutical strategies involving oocyte microsurgery. Trial registration number not applicable

P–186 Volumetric imaging provides insight into the 3D ultrastructural organization of maturing human oocytes

Abstract Study question Is volume electron microscopy suitable for high-resolution oocyte imaging in three dimensions (3D)? Summary answer Focused Ion Bean Scanning Electron Microscopy (FIB-SEM) allows 3D visualization and quantitative analysis of ultrastructural features in the large human oocyte volume. What is known already Transmission electron microscopy (TEM) has been traditionally used to study the fine morphology of female gametes. However, 2D micrographs provide only limited information about the topology of subcellular structures. Volumetric studies are needed to elucidate the 3D organization of complex oocyte cytoplasm. Study design, size, duration An academic study conducted in collaboration with an IVF clinic. Advanced 3D - ultrastructural analysis was performed on 9 human oocytes representing 3 stages of maturation (3 germinal vesicle (GV), 3 metaphase I (MI), 3 metaphase (MII) oocytes) collected from February 2018 to November 2019. Participants/materials, setting, methods Spare IVF oocytes, donated by 9 young egg donors (22–29 years), were cultured in vitro until they reached a defined developmental stage. Each oocytés meiotic status was determined based on the presence/absence of a prophase nucleus, a polar body, and a non-invasively detectable MI/MII spindle. Following standard TEM preparation, individual oocyte-containing resin blocks were coated with a thin carbon layer[JJ1] , mounted on the microscope stage, and subjected to FIB-SEM imaging. Main results and the role of chance FIB-SEM tomography provided an unprecedented view of the oocytés intracellular morphology. Automated serial scanning of newly exposed sample surface generated large stacks (120–1294 slices) of ultrastructural images with 40–100 nm z-resolution. The tomographic reconstruction of acquired datasets revealed the spatial arrangement of inner oocyteś structures. The imaging protocol was optimized to ensure sufficient image detail, minimal noisiness, and time-efficiency of large volume scanning. Comparison of oocytes fixed at different maturation stages confirmed previous TEM observations that the cortical region of GV oocytes is deprived of membraneous structures, and major organelle redistribution occurs during the MI phase. Semi-automated 3D image segmentation was employed to distinguish distinct organelle populations and evaluated their abundance. Subsequent quantitative analysis of volumetric data showed that the mitochondrion occupies ∼ 5.27% of MII oocyte volume. In conclusion, the volumetric imaging, followed by advanced image analysis, maximizes the amount of morphological data obtained from a single human oocyte. Limitations, reasons for caution The imaging procedure was pioneered on a small number of hormonally-primed oocytes, which failed to complete development in vivo. There is a trade-off between resolution, the size of the 3D volume, and imaging time. Block-face ion milling during FIB-SEM imaging inevitably results in sample destruction. Wider implications of the findings: This proof-of-concept study opens up new possibilities to study the delicate architecture of scarce human oocytes. Enhancing our knowledge of the spatial organization of ooplasm is pivotal for developing experimental and therapeutical strategies involving oocyte microsurgery. Trial registration number Not applicable

Dosimetric Quantities in Neuroendocrine Tumors over Treatment Cycles with 177Lu-DOTATATE.

Tumor dosimetry was performed for 177Lu-DOTATATE with the aims of better understanding the range and variation of the tumor-absorbed doses (ADs), how different dosimetric quantities evolve over the treatment cycles, and whether this evolution differs depending on the tumor grade. Such information is important for radiobiologic interpretation and may inform the design of alternative administration schemes. Methods: The data came from 41 patients with neuroendocrine tumors (NETs) of grade 1 (n = 23) or 2 (n = 18) who had received between 2 and 9 treatment cycles. Dosimetry was performed for 182 individual lesions, giving a total of 880 individual AD assessments across all cycles. Hybrid planar-SPECT/CT imaging was used, including quantitative SPECT reconstruction, voxel-based absorbed-dose-rate calculation, semiautomatic image segmentation, and partial-volume correction. Linear mixed-effect models were used to analyze changes in tumor ADs over cycles, absorbed-dose rates and activity concentrations on day 1, effective half-times, and tumor volumes. Tumors smaller than 8 cm3 were excluded from analyses. Results: Tumor ADs ranged between 2 and 77 Gy per cycle. On average, the AD decreased over the cycles, with significantly different rates (P < 0.05) of 6% and 14% per cycle for grade 1 and 2 NETs, respectively. The absorbed-dose rates and activity concentrations on day 1 decreased by similar amounts. The effective half-times were less variable but shorter for grade 2 than for grade 1 (P < 0.001). For grade 2 NETs, the tumor volumes decreased, with a similar tendency in grade 1. Conclusion: The tumor AD, absorbed-dose rate, and activity uptake decrease, in parallel with tumor volumes, between 177Lu-DOTATATE treatment cycles, particularly for grade 2 NETs. The effective half-times vary less but are lower for grade 2 than grade 1 NETs. These results may indicate the development of radiation-induced fibrosis and could have implications for the design of future treatment and dosimetry protocols.