The feasibility of measuring regional lung perfusion with a conventional CT scanner

Bibliography: p. 162-17

Dosimetric verification of micro-MLC based intensity modulated radiation therapy

A methodology for the dosimetric verification of micro-multileaf collimator (mMLC) based intensity modulated radiation therapy (IMRT) plans intended for stereotactic applications is described. The method is similar to that of conventional IMRT patient-specific quality assurance (QA) with some notable exceptions, particularly, mechanical tests that verify the mMLC positioning with respect to the isocenter and individual leaf calibration prior to use. Relative dosimetry measurements are performed with radiographic film, a commercial film-scanning system and a doseimage registration program. Film dosimetry results are within ± 3.0 % of calculated distributions or within 2.0 mm distance to agreement. Absolute dosimetry measurements are performed with a small volume ion chamber and a commercially available stereotactic phantom. The cumulative dose from all beams is within ± 2.0 % of the prescribed dose. Large deviations may be observed from individual beams since the smaller IMRT fields tend to have very few high-dose and low-gradient regions. An independent program that examines the treatment mMLC file is used to estimate the central axis dose from each beam and provide a dose image that can be assessed alongside the intended fluence distribution prior to treatment. Tolerances for relative and absolute dosimetry of mMLC-based IMRT treatments are tighter than what is typically reported for conventional MLC-based IMRT. Also, the time commitment for the IMRT QA is slightly longer than of conventional MLC-based IMRT due to QA processes that check the mechanical alignment of the mMLC device with the laser and radiation isocenter

An increase in retractions of research publications is an issue for Medical Physics

Medical physics can be one of the most rewarding applications of physics in day-to-day society's life. Hence, translational research in this particular field attracts a lot of interest given its potential impact on the delivery of patient care. The credibility of scientific discoveries and research outcomes reported in the scientific literature is driven by confidence in the integrity of scientists performing this research. However, scientific misconduct often blackens the image of scientific research and negatively impacts the faith society has in science

Machine learning of feline GI disorders using abdominal ultrasound images

Machine learning of feline GI disorders using abdominal ultrasound image

Unsupervised Few Shot Key Frame Extraction for Cow Teat Videos

A novel method of monitoring the health of dairy cows in large-scale dairy farms is proposed via image-based analysis of cows on rotary-based milking platforms, where deep learning is used to classify the extent of teat-end hyperkeratosis. The videos can be analyzed to segment the teats for feature analysis, which can then be used to assess the risk of infections and other diseases. This analysis can be performed more efficiently by using the key frames of each cow as they pass through the image frame. Extracting key frames from these videos would greatly simplify this analysis, but there are several challenges. First, data collection in the farm setting is harsh, resulting in unpredictable temporal key frame positions; empty, obfuscated, or shifted images of the cow’s teats; frequently empty stalls due to challenges with herding cows into the parlor; and regular interruptions and reversals in the direction of the parlor. Second, supervised learning requires expensive and time-consuming human annotation of key frames, which is impractical in large commercial dairy farms housing thousands of cows. Unsupervised learning methods rely on large frame differences and often suffer low performance. In this paper, we propose a novel unsupervised few-shot learning model which extracts key frames from large (∼21,000 frames) video streams. Using a simple L1 distance metric that combines both image and deep features between each unlabeled frame and a few (32) labeled key frames, a key frame selection mechanism, and a quality check process, key frames can be extracted with sufficient accuracy (F score 63.6%) and timeliness (<10 min per 21,000 frames) for commercial dairy farm setting demands

Separable Confident Transductive Learning for Dairy Cows Teat-End Condition Classification

Teat-end health assessments are crucial to maintain milk quality and dairy cow health. One approach to automate teat-end health assessments is by using a convolutional neural network to classify the magnitude of teat-end alterations based on digital images. This approach has been demonstrated as feasible with GoogLeNet but there remains a number of challenges, such as low performance and comparing performance with different ImageNet models. In this paper, we present a separable confident transductive learning (SCTL) model to improve the performance of teat-end image classification. First, we propose a separation loss to ameliorate the inter-class dispersion. Second, we generate high confident pseudo labels to optimize the network. We further employ transductive learning to narrow the gap between training and test datasets with categorical maximum mean discrepancy loss. Experimental results demonstrate that the proposed SCTL model consistently achieves higher accuracy across all seventeen different ImageNet models when compared with retraining of original approaches

Radiomics Modeling of Catastrophic Proximal Sesamoid Bone Fractures in Thoroughbred Racehorses Using &mu;CT

Proximal sesamoid bone (PSB) fractures are the most common musculoskeletal injury in race-horses. X-ray CT imaging can detect expressed radiological features in horses that experienced catastrophic fractures. Our objective was to assess whether expressed radiomic features in the PSBs of 50 horses can be used to develop machine learning models for predicting PSB fractures. The &mu;CTs of intact contralateral PSBs from 50 horses, 30 of which suffered catastrophic fractures, and 20 controls were studied. From the 129 intact &mu;CT images of PSBs, 102 radiomic features were computed using a variety of voxel resampling dimensions. Decision Trees and Wrapper methods were used to identify the 20 top expressed features, and six machine learning algorithms were developed to model the risk of fracture. The accuracy of all machine learning models ranged from 0.643 to 0.903 with an average of 0.754. On average, Support Vector Machine, Random Forest (RUS Boost), and Log-regression models had higher performance than K-means Nearest Neighbor, Neural Network, and Random Forest (Bagged Trees) models. Model accuracy peaked at 0.5 mm and decreased substantially when the resampling resolution was greater than or equal to 1 mm. We find that, for this in vitro dataset, it is possible to differentiate between unfractured PSBs from case and control horses using &mu;CT images. It may be possible to extend these findings to the assessment of fracture risk in standing horses