SimCol3D - 3D reconstruction during colonoscopy challenge.

Colorectal cancer is one of the most common cancers in the world. While colonoscopy is an effective screening technique, navigating an endoscope through the colon to detect polyps is challenging. A 3D map of the observed surfaces could enhance the identification of unscreened colon tissue and serve as a training platform. However, reconstructing the colon from video footage remains difficult. Learning-based approaches hold promise as robust alternatives, but necessitate extensive datasets. Establishing a benchmark dataset, the 2022 EndoVis sub-challenge SimCol3D aimed to facilitate data-driven depth and pose prediction during colonoscopy. The challenge was hosted as part of MICCAI 2022 in Singapore. Six teams from around the world and representatives from academia and industry participated in the three sub-challenges: synthetic depth prediction, synthetic pose prediction, and real pose prediction. This paper describes the challenge, the submitted methods, and their results. We show that depth prediction from synthetic colonoscopy images is robustly solvable, while pose estimation remains an open research question.

Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy.

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding techniques.

Polyp Segmentation with Fully Convolutional Deep Neural Networks-Extended Evaluation Study.

Analysis of colonoscopy images plays a significant role in early detection of colorectal cancer. Automated tissue segmentation can be useful for two of the most relevant clinical target applications-lesion detection and classification, thereby providing important means to make both processes more accurate and robust. To automate video colonoscopy analysis, computer vision and machine learning methods have been utilized and shown to enhance polyp detectability and segmentation objectivity. This paper describes a polyp segmentation algorithm, developed based on fully convolutional network models, that was originally developed for the Endoscopic Vision Gastrointestinal Image Analysis (GIANA) polyp segmentation challenges. The key contribution of the paper is an extended evaluation of the proposed architecture, by comparing it against established image segmentation benchmarks utilizing several metrics with cross-validation on the GIANA training dataset. Different experiments are described, including examination of various network configurations, values of design parameters, data augmentation approaches, and polyp characteristics. The reported results demonstrate the significance of the data augmentation, and careful selection of the method's design parameters. The proposed method delivers state-of-the-art results with near real-time performance. The described solution was instrumental in securing the top spot for the polyp segmentation sub-challenge at the 2017 GIANA challenge and second place for the standard image resolution segmentation task at the 2018 GIANA challenge.

Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge.

Colonoscopy is the gold standard for colon cancer screening though some polyps are still missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection sub-challenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks are the state of the art. Nevertheless, it is also demonstrated that combining different methodologies can lead to an improved overall performance.

Gland segmentation in colon histology images: The glas challenge contest.

Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter-observer as well as intra-observer reproducibility of cancer grading is still a major challenge in modern pathology. An automated approach which quantifies the morphology of glands is a solution to the problem. This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI'2015. Details of the challenge, including organization, dataset and evaluation criteria, are presented, along with the method descriptions and evaluation results from the top performing methods.

Assessment of algorithms for mitosis detection in breast cancer histopathology images.

The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues. In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists.

Comparison of different grid of tags detection methods in tagged cardiac MR imaging.

PURPOSE: Non-invasive imaging assessment of cardiac function is important in cardiovascular disease diagnosis, especially for evaluation of local cardiac motion. Tagged cardiac MRI has been developed for this purpose, but evaluation of the results requires quantification and automation. METHODS: Two methods utilizing active contour modeling for wall motion extraction based on tagged cardiac MRI scans were evaluated based on properties of tracking methods in the image domain and frequency domain. Three criteria were used: accuracy, inter-subject and intra-subject sensitivity. The tracking results were evaluated by a medical expert. The evaluation methodology and its possible generalization to other diagnostic methods were considered. RESULTS: Image domain and frequency domain analysis of tagged cardiac MRI data sets were evaluated demonstrating that the image domain method provides better results. The image domain method method is much more resistant to changes in the data, this time, due to a different subject being scanned. The frequency domain approach is not suitable for clinical applications, as the global error is significantly increased (more than 20%). CONCLUSION: The image domain method was found most effective, and it can generate a set of clearly identified parameters. The evaluation approach can be an interesting alternative to classical psychovisual studies which are time-consuming and often fastidious for clinicians.

Segmentation of myocardial boundaries in tagged cardiac MRI using active contours: a gradient-based approach integrating texture analysis.

The noninvasive assessment of cardiac function is of first importance for the diagnosis of cardiovascular diseases. Among all medical scanners only a few enables radiologists to evaluate the local cardiac motion. Tagged cardiac MRI is one of them. This protocol generates on Short-Axis (SA) sequences a dark grid which is deformed in accordance with the cardiac motion. Tracking the grid allows specialists a local estimation of cardiac geometrical parameters within myocardium. The work described in this paper aims to automate the myocardial contours detection in order to optimize the detection and the tracking of the grid of tags within myocardium. The method we have developed for endocardial and epicardial contours detection is based on the use of texture analysis and active contours models. Texture analysis allows us to define energy maps more efficient than those usually used in active contours methods where attractor is often based on gradient and which were useless in our case of study, for quality of tagged cardiac MRI is very poor.

Development and evaluation of a noninvasive marker cluster technique to assess three-dimensional kinematics of the distal portion of the forelimb in horses.

OBJECTIVE: To develop and evaluate a marker cluster set for measuring sagittal and extrasagittal movement of joints in the distal portion of the forelimb in ponies. ANIMALS: 4 ponies. PROCEDURES: 5 infrared cameras were positioned on a concrete walkway in a frontal-sagittal arc and calibrated. Four segments were defined: hoof, middle phalanx, proximal phalanx, and metacarpus. Rigid clusters with 4 retroreflective markers were placed on each segment. A static trial was recorded with additional anatomic markers on the medial and lateral joint lines. Those anatomic markers were removed, and kinematic data were recorded at 240 Hz during walking. An ensemble mean was computed from the 4 ponies from 5 replicates of the walks. Joint kinematic variables were calculated by use of the calibrated anatomical system technique. The design and error dispersion of each marker were evaluated. RESULTS: Marker clusters were quasiplanar, but variation in orientation error was reduced because the mean radii were > 10 times the largest error dispersion values. Measurements of sagittal rotations of the distal interphalangeal, proximal interphalangeal, and metacarpophalangeal joints were similar to measurements obtained with bone-fixed triads, but larger discrepancies between the 2 methods were found for extrasagittal rotations. CONCLUSIONS AND CLINICAL RELEVANCE: Development of noninvasive methods for quantifying data pertaining to 3-dimensional motion in horses is important for advancement of clinical analysis. The technique used in the study enabled identification of flexion-extension motions with an acceptable degree of accuracy. Appropriate correction algorithms and improvements to the technique may enable future quantification of extrasagittal motions.

Determination of a prostaglandin D2 antagonist and its acyl glucuronide metabolite in human plasma by high performance liquid chromatography with tandem mass spectrometric detection--a lack of MS/MS selectivity between a glucuronide conjugate and a phase I metabolite.

A method for the determination of a prostaglandin D(2) receptor antagonist (I, a compound being evaluated for the prevention of niacin induced flushing) and its acyl glucuronide metabolite (II) in human plasma is presented. The method utilized high performance liquid chromatography (HPLC) with tandem mass spectrometric (MS/MS) detection using an atmospheric pressure chemical ionization (APCI) interface operated in the positive ionization mode. The product ion was a radical cation generated via a homolytic bond cleavage. A chemical analog of the drug was used as internal standard (III). The acyl glucuronide metabolite (II) was detected using the same precursor-to-product ion transition used for the parent compound after chromatographic separation of I and II. Drug and metabolite were extracted using semi-automated, 96-well format solid phase extraction (SPE), and chromatography was performed using a reverse phase analytical column with an isocratic mobile phase. The chromatographic retention factor (k') of II was found to be highly sensitive to mobile phase formic acid concentration. An adjustment in mobile phase formic acid concentration improved the chromatographic separation between II and a mono-hydroxylated metabolite after an unexpected lack of MS/MS selectivity between the two molecules was observed. The dependence of retention factor on formic acid concentration (k' increased as formic acid concentration decreased) was thought to indicate polar interactions between II and the stationary phase. The stability of II in spiked human plasma was determined. The rate of hydrolysis back to parent compound was relatively low (approximately 0.1 and 0.5% per hour at room temperature and 4 degrees C, respectively) indicating that significant changes in analyte concentrations did not occur during sample processing. The concentration range of the assay was 10-2500 ng/mL for both drug and glucuronide metabolite.

Chiral separation of labetalol stereoisomers in human plasma by capillary electrophoresis.

A newly derivatized cyclodextrin [octakis-(2,3-diacetyl-6-sulfato)-gamma-cyclodextrin] was investigated as a chiral selector in capillary zone electrophoresis in a study of the chiral separation of labetalol stereoisomers. Heptakis(2,3-diacetyl-6-sulfato)-beta-cyclodextrin (HDAS-beta-CD) and octakis(2,3-diacetyl-6-sulfato)-gamma-cyclodextrin (ODAS-gamma-CD) were shown to be effective in separating labetalol stereoisomers. Optimal separating conditions of the four stereoisomers of labetalol were achieved with 10 mM HDAS-beta-CD and 10 mM ODAS-gamma-CD in an acidic pH buffer of low molarity. Data illustrating the effects of capillary length and cyclodextrin concentration on the separation are presented. The longer capillary length and high voltage enabled the baseline separation of all isomers in less than 15 min. The optimized method was applied to the analysis of human control plasma containing labetalol utilizing solid-phase extraction (SPE) in the 96-well format.

Determination of simvastatin-derived HMG-CoA reductase inhibitors in biomatrices using an automated enzyme inhibition assay with radioactivity detection.

A robust, automated enzyme inhibition assay method was developed and validated for the determination of HMG-CoA reductase inhibitory activities in plasma and urine samples following simvastatin (SV) administration. The assay was performed on Tecan Genesis 150 and 200 systems equipped with 8-probe and 96-well plates. Plasma samples containing HMG-CoA reductase inhibitors were treated with acetonitrile for protein precipitation before being incubated with HMG-CoA reductase, [14C]-HMG-CoA, and NADPH for a fixed length of time at a fixed temperature. The product, [14C]-mevalonic acid, was lactonized and separated from excess substrate via a small ion exchange resin column, and radioactivity was counted on a scintillation counter. HMG-CoA reductase inhibitors were measured before and after base hydrolysis. The two values obtained for each sample are referred to as 'active' and 'total' HMG-CoA reductase inhibitor concentrations. Simvastatin acid (SVA), the beta-hydroxy acid of SV, was used as a standard to generate a calibration curve of HMG-CoA reductase activity versus SVA concentration (ng/ml). Three calibration ranges, 0.4-20, 2-50, and 50, 100 ng/ml, in human and animal plasma and urine were validated. The assay precision was less than 8.5%, CV in plasma and less than 10.4% in urine. The assay accuracy was 93.6-103.0 and 98.1-103.9% for the 0.4 20 and 2-50 ng/ml calibration ranges, respectively, in human plasma, and was 97.3-105.1, 94.4- 105.2, and 90.2-95.7%, for calibration range 5-100 ng/ml in rat plasma, dog plasma and human urine, respectively.

Robotic inhibition assay for determination of HMG-CoA reductase inhibitors in human plasma.

The cholesterol-lowering drug simvastatin (SIMV, Zocor reduced heart attacks by 42% in patients who had high cholesterol levels and suffered from heart disease. Upon oral administration, SIMV is quickly hydrolyzed to its beta-hydroxyacid and other acid metabolites, which are potent inhibitors of 3-hydroxy-3-methylglutaryl coenzyme A reductase. A Tecan-based enzyme inhibition assay has been developed to improve the existing Zymark-based assay for the determination of both active and total concentrations of HMG-CoA reductase inhibitors in human plasma. A Tecan Genesis 200 robotic workstation equipped with eight probes and customized hardware was utilized to achieve higher sample throughput and improve assay reproducibility and mechanical stability. The developed enzyme inhibition assay was validated over two concentration ranges of 0.4-20 ng equivalent/mL, and 2-50 ng equivalent/mL. Intra- and interday precision data (coefficient of variation (CV)) for both concentration ranges were less than 9%, with an accuracy of 93-107%. The interday precision for the determination of quality control (QC) samples was less than 2% and 8%, respectively. The respective interday QC accuracy values were 93-103% and 97-104%. Good linearity across the two concentration ranges was observed, with acceptable reproducibility. This improved enzyme inhibition assay has been utilized to analyze human plasma samples from several clinical studies.

Determination of a peptide-doxorubicin, prostate-specific antigen activated prodrug, and its active metabolites in human plasma using high-performance liquid chromatography with fluorescence detection. Stabilization of the peptide prodrug with EDTA.

A method for the determination of I, a peptide-doxorubicin conjugate that was evaluated for the treatment of prostate cancer, and two of its active metabolites, doxorubicin and leucine-doxorubicin is described. Blood samples were chilled immediately after being drawn in order to prevent ex vivo entry of the metabolites into red blood cells. EDTA (10 mg/ml final concentration) was used to prevent plasma-mediated degradation of the peptide portion of the prodrug. After the addition of internal standard, plasma was prepared for analysis using a C-8 solid-phase extraction column. In order to overcome secondary ionic interactions with the silica-based extraction column, the analytes were eluted with ammonium hydroxide in methanol. The extracts were evaporated to dryness, reconstituted, and assayed by step change, gradient, reverse phase HPLC with fluorescence detection. Two interfering metabolites found in post dose plasma were chromatographically separated by an adjustment of the mobile phase pH. The within-day reproducibility of the doxorubicin and leucine-doxorubicin chromatographic retention times was improved by a brief washing of the analytical column with 90% acetonitrile after each injection. The range of the standard curve was 12.5-1250 ng/ml for doxorubicin and 25-2500 ng/ml for I and leucine-doxorubicin.

Quantitative determination of a selective alpha-1a receptor antagonist in human plasma by high-performance liquid chromatography with tandem mass spectrometric detection.

Solid-phase extraction, utilizing a 96-well plate format, was used to isolate an alpha-1a receptor antagonist and internal standard from human plasma. Following the isolation procedure, the analyte and internal standard were separated and detected using reversed-phase HPLC coupled with atmospheric pressure chemical ionization (APCI) mass spectrometry operated in the positive ion multiple reaction monitoring (MRM) mode. Based upon the peak area ratio (analyte: internal standard) the analyte was quantified over a concentration range of 0.02-2 ng/ml. Assay validation results including parameters such as precision and accuracy are presented. The validated method was subsequently used to support human pharmacokinetic studies.