MRI segmentation: methods and applications

The current literature on MRI segmentation methods is reviewed. Particular emphasis is placed on the relative merits of single image versus multispectral segmentation, and supervised versus unsupervised segmentation methods. Image pre-processing and registration are discussed, as well as methods of validation. The application of MRI segmentation for tumor volume measurements during the course of therapy is presented here as an example, illustrating problems associated with inter- and intra-observer variations inherent to supervised methods.

Comparison of supervised MRI segmentation methods for tumor volume determination during therapy

Two different multispectral pattern recognition methods are used to segment magnetic resonance images (MRI) of the brain for quantitative estimation of tumor volume and volume changes with therapy. A supervised k-nearest neighbor (kNN) rule and a semi-supervised fuzzy c-means (SFCM) method are used to segment MRI slice data. Tumor volumes as determined by the kNN and SFCM segmentation methods are compared with two reference methods, based on image grey scale, as a basis for an estimation of ground truth, namely: (a) a commonly used seed growing method that is applied to the contrast enhanced T1-weighted image, and (b) a manual segmentation method using a custom-designed graphical user interface applied to the same raw image (T1-weighted) dataset. Emphasis is placed on measurement of intra and inter observer reproducibility using the proposed methods. Intra- and interobserver variation for the kNN method was 9% and 5%, respectively. The results for the SFCM method was a little better at 6% and 4%, respectively. For the seed growing method, the intra-observer variation was 6% and the interobserver variation was 17%, significantly larger when compared with the multispectral methods. The absolute tumor volume determined by the multispectral segmentation methods was consistently smaller than that observed for the reference methods. The results of this study are found to be very patient case-dependent. The results for SFCM suggest that it should be useful for relative measurements of tumor volume during therapy, but further studies are required. This work demonstrates the need for minimally supervised or unsupervised methods for tumor volume measurements.

Monitoring brain tumor response to therapy using MRI segmentation

The performance evaluation of a semi-supervised fuzzy c-means (SFCM) clustering method for monitoring brain tumor volume changes during the course of routine clinical radiation-therapeutic and chemo-therapeutic regimens is presented. The tumor volume determined using the SFCM method was compared with the volume estimates obtained using three other methods: (a) a k nearest neighbor (kNN) classifier, b) a grey level thresholding and seed growing (ISG-SG) method and c) a manual pixel labeling (GT) method for ground truth estimation. The SFCM and kNN methods are applied to the multispectral, contrast enhanced T1, proton density, and T2 weighted, magnetic resonance images (MRI) whereas the ISG-SG and GT methods are applied only to the contrast enhanced T1 weighted image. Estimations of tumor volume were made on eight patient cases with follow-up MRI scans performed over a 32 week interval during treatment. The tumor cases studied include one meningioma, two brain metastases and five gliomas. Comparisons with manually labeled ground truth estimations showed that there is a limited agreement between the segmentation methods for absolute tumor volume measurements when using images of patients after treatment. The average intraobserver reproducibility for the SFCM, kNN and ISG-SG methods was found to be 5.8%, 6.6% and 8.9%, respectively. The average of the interobserver reproducibility of these methods was found to be 5.5%, 6.5% and 11.4%, respectively. For the measurement of relative change of tumor volume as required for the response assessment, the multi-spectral methods kNN and SFCM are therefore preferred over the seedgrowing method.

Normal brain volume measurements using multispectral MRI segmentation

The performance of a supervised k-nearest neighbor (kNN) classifier and a semisupervised fuzzy c-means (SFCM) clustering segmentation method are evaluated for reproducible measurement of the volumes of normal brain tissues and cerebrospinal fluid. The stability of the two segmentation methods is evaluated for (a) operator selection of training data, (b) reproducibility during repeat imaging sessions to determine any variations in the sensor performance over time, (c) variations in the measured volumes between different subjects, and (d) variability with different imaging parameters. The variations were found to be dependent on the type of measured tissue and the operator performing the segmentations. The variability during repeat imaging sessions for the SFCM method was < 3%. The absolute volumes of the brain matter and cerebrospinal fluid between subjects varied quite large, ranging from 9% to 13%. The intraobserver and interobserver reproducibility for SFCM were < 4% for the soft tissues and 6% for cerebrospinal fluid. The corresponding results for the kNN segmentation method were higher compared to the SFCM method.

Antimicrobial properties of dentine bonding agents determined using in vitro and ex vivo methods

AIMS

To determine the antimicrobial properties of a selection of dentine bonding agents [DBAs] using the disc diffusion and direct contact methods and an ex vivo method using extracted carious permanent molar teeth.

METHODS

DBAs (n=15) were tested using Streptococcus mutans UA159 in disc diffusion and direct contact methods. In the ex vivo study 6 DBAs were selected and pre- and post-treatment samples of carious dentine (n< or =12) were taken. Samples were also taken post-acid-etching. The number of microorganisms in dentine sample was determined and compared.

RESULTS

The inhibition zones and percent growth inhibition were related to the pH of the culture medium containing the DBA (p<0.01). Clearfill Protect Bond exhibited the greatest bacterial killing followed by ibond (99.8%+/-0.08 and 98.2+/-1.4, respectively). The phosphoric acid etchant alone resulted in an 83% killing. The in vitro tests results did not correlate. The ex vivo killing reflected the percent growth inhibition observed in the direct contact method.

CONCLUSION

A guide to the potential antimicrobial activity of a DBA may be gained from an assessment of its pH when added to bacteriological culture medium. The direct contact method gives a better reflection of the killing of bacteria in infected dentine than the disk diffusion method. Killing in the ex vivo model gives a more realistic and more reliable method for determining the antibacterial activity of a given DBA and that comparisons of the relative inhibitory activity of DBAs should be tested using this ex vivo model.

Cascaded optical parametric oscillations

We have observed the spectral and temporal characteristics of a 1.064-mm pumped, continuously tunable optical parametric oscillator (OPO) using a periodically poled lithium niobate nonlinear crystal. An efficient secondary OPO pumped by the resonant signal of the primary OPO was observed. Predictions obtained from theory agree with observed results and provide insight into the dynamics of pulsed parametric oscillation. 1997 Optical Society of America.

Systematic review protocol to assess artificial intelligence diagnostic accuracy performance in detecting acute ischaemic stroke and large-vessel occlusions on CT and MR medical imaging

INTRODUCTION

The use of artificial intelligence (AI) to support the diagnosis of acute ischaemic stroke (AIS) could improve patient outcomes and facilitate accurate tissue and vessel assessment. However, the evidence in published AI studies is inadequate and difficult to interpret which reduces the accountability of the diagnostic results in clinical settings. This study protocol describes a rigorous systematic review of the accuracy of AI in the diagnosis of AIS and detection of large-vessel occlusions (LVOs).

METHODS AND ANALYSIS

We will perform a systematic review and meta-analysis of the performance of AI models for diagnosing AIS and detecting LVOs. We will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols guidelines. Literature searches will be conducted in eight databases. For data screening and extraction, two reviewers will use a modified Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. We will assess the included studies using the Quality Assessment of Diagnostic Accuracy Studies guidelines. We will conduct a meta-analysis if sufficient data are available. We will use hierarchical summary receiver operating characteristic curves to estimate the summary operating points, including the pooled sensitivity and specificity, with 95% CIs, if pooling is appropriate. Furthermore, if sufficient data are available, we will use Grading of Recommendations, Assessment, Development and Evaluations profiler software to summarise the main findings of the systematic review, as a summary of results.

ETHICS AND DISSEMINATION

There are no ethical considerations associated with this study protocol, as the systematic review focuses on the examination of secondary data. The systematic review results will be used to report on the accuracy, completeness and standard procedures of the included studies. We will disseminate our findings by publishing our analysis in a peer-reviewed journal and, if required, we will communicate with the stakeholders of the studies and bibliographic databases.

PROSPERO REGISTRATION NUMBER

CRD42020179652.

Contactless monitoring of respiratory rate (RR) and heart rate (HR) in non-acuity settings: a clinical validity study

OBJECTIVE

Patient monitoring in general wards primarily involves intermittent observation of temperature, heart rate (HR), respiratory rate (RR) and blood pressure performed by the nursing staff. Several hours can lapse between such measurements, and the patient may go unobserved. Despite the growing widespread use of sensors to monitor vital signs and physical activities of healthy individuals, most acutely ill hospitalised patients remain unmonitored, leaving them at an increased risk. We investigated whether a contactless monitoring system could measure vital parameters, such as HR and RR, in a real-world hospital setting.

DESIGN

A cross-sectional prospective study.

SETTING AND PARTICIPANTS

We examined the suitability of employing a non-contact monitoring system in a low-acuity setup at a tertiary care hospital in India. Measurements were performed on 158 subjects, with data acquired through contactless monitoring from the general ward and dialysis unit.

OUTCOME MEASURES

Vital parameters (RR and HR) were measured using a video camera in a non-acuity setting.

RESULTS

Three distinct combinations of contactless monitoring afforded excellent accuracy. Contactless RR monitoring was linearly correlated with Alice NightOne and manual counts, presenting coefficients of determination of 0.88 and 0.90, respectively. Contactless HR monitoring presented a coefficient of determination of 0.91. The mean absolute errors were 0.84 and 2.15 beats per minute for RR and HR, respectively.

CONCLUSIONS

Compared with existing Food and Drug Administration-approved monitors, the findings of the present study revealed that contactless monitoring of RR and HR accurately represented study populations in non-acuity settings. Contactless video monitoring is an unobtrusive and dependable method for monitoring and recording RR and HR. Further research is needed to validate its dependability and utility in other settings, including acute care.

TRIAL REGISTRATION NUMBER

CTRI/2018/11/016246.

Intrapulse temporal and wavelength shifts of a high-power 2.1-µm Ho:YAG laser and their potential influence on atmospheric lidar measurements

A high-power, flash-lamp-pumped, Q-switched Ho:YAG laser has been developed to produce up to 150 mJ in a 100-ns Q-switched pulse. The Ho laser was initially used in a direct detection lidar-differential absorption lidar (DIAL) system to measure vertical density profiles of aerosols and water vapor in the atmosphere. It was found, however, that the Ho laser operated simultaneously on two closely spaced spectral emission wavelengths (2.090 and 2.097 µm) and that the distribution of energy between the two wavelengths could change significantly on time scales of several seconds to minutes. Such intrapulse temporal and wavelength shifts were found to alter the atmospheric lidar return significantly because one of the laser lines coincided with a water vapor absorption line in the atmosphere. This laser spectral output problem was overcome by the use of intracavity étalons that controlled the laser spectral-temporal characteristics but reduced the laser output energy to approximately 75 mJ/pulse in a 100-ns pulse length. These results are important as they serve to point out the difficulties of developing and using a high-power 2.1- µm Ho laser for atmospheric lidar when high-resolution spectral and temporal characteristics can significantly influence the lidar return and be misinterpreted as resulting from atmospheric signals.

Tunable 17-microm laser spectrometer for optical absorption measurements of CH(4), C(2)H(4), and high-temperature HCl

A tunable 1.7-microm cw F(2)(+)-center laser spectrometer system that is continuously tunable from 1.43 to 1.78 microm in wavelength and has aspectral linewidth (FVWM) of 0.07 cm(-1) is developed. This system is used to measure the 2-0 rotational-vibrational absorption line profiles of hydrogen chloride (HCl) at high temperatures, the Boltzmann thermal equilibrium temperature, and to determine the extent of potential interference or overlap of the HCl lines with those that are due to hot water vapor. As a demonstration of the utility of the laser spectrometer system, it is also used to measure the spectra of methane (CH(4)) and ethylene (C(2)H(4)) near 1.65 microm, and is routed through a fiber-optic cable to a remote site to detect a CH(4) plume.