Machine Learning towards General Medical Image Segmentation

The quality of patient care associated with diagnostic radiology is proportionate to a physician\u27s workload. Segmentation is a fundamental limiting precursor to diagnostic and therapeutic procedures. Advances in machine learning aims to increase diagnostic efficiency to replace single applications with generalized algorithms. We approached segmentation as a multitask shape regression problem, simultaneously predicting coordinates on an object\u27s contour while jointly capturing global shape information. Shape regression models inherent point correlations to recover ambiguous boundaries not supported by clear edges and region homogeneity. Its capabilities was investigated using multi-output support vector regression (MSVR) on head and neck (HaN) CT images. Subsequently, we incorporated multiplane and multimodality spinal images and presented the first deep learning multiapplication framework for shape regression, the holistic multitask regression network (HMR-Net). MSVR and HMR-Net\u27s performance were comparable or superior to state-of-the-art algorithms. Multiapplication frameworks bridges any technical knowledge gaps and increases workflow efficiency

Classification of blast cell type on acute myeloid leukemia (AML) based on image morphology of white blood cells

AML is one type of cancer of the blood and spinal cord. AML has a number of subtypes including M0 and M1. Both subtypes are distinguished by the dominant blast cell type in the WBC, the myeloblast cells, promyelocyte, and myelocyte. This makes the diagnosis process of leukemia subtype requires identification of blast cells in WBC. Automatic blast cell identification is widely developed but is constrained by the lack of data availability, and uneven distribution for each type of blast cell, resulting in problems of data imbalance. This makes the system developed has poor performance. This study aims to classify blast cell types in WBC identified AML-M0 and AML-M1. The method used is divided into two stages, first pre-processing, image segmentation and feature extraction. The second stage, perform resample, which is continued over sampling with SMOTE. The process is done until the amount of data obtained is relatively the same for each blast cell, then the process of elimination of data duplication, randomize, classification and performance measurement. The validation method used is k-fold cross-validation with k=10. Performance parameters used are sensitivity, specifyicity, accuracy, and AUC. The average performance resulting from classification of cell types in AML with Random Forest algorithm obtained 82.9% sensitivity, 92.1% specificity, 89.6% accuracy and 87.5% AUC. These results indicate a significant improvement compared to the system model without using SMOTE. The performance generated by reference to the AUC value, the proposed system model belongs to either category, so it can be used for further stages of leukemia subtype AML-M0 and AML-M1

Three-dimensional Segmentation of the Scoliotic Spine from MRI using Unsupervised Volume-based MR-CT Synthesis

Vertebral bone segmentation from magnetic resonance (MR) images is a challenging task. Due to the inherent nature of the modality to emphasize soft tissues of the body, common thresholding algorithms are ineffective in detecting bones in MR images. On the other hand, it is relatively easier to segment bones from CT images because of the high contrast between bones and the surrounding regions. For this reason, we perform a cross-modality synthesis between MR and CT domains for simple thresholding-based segmentation of the vertebral bones. However, this implicitly assumes the availability of paired MR-CT data, which is rare, especially in the case of scoliotic patients. In this paper, we present a completely unsupervised, fully three-dimensional (3D) cross-modality synthesis method for segmenting scoliotic spines. A 3D CycleGAN model is trained for an unpaired volume-to-volume translation across MR and CT domains. Then, the Otsu thresholding algorithm is applied to the synthesized CT volumes for easy segmentation of the vertebral bones. The resulting segmentation is used to reconstruct a 3D model of the spine. We validate our method on 28 scoliotic vertebrae in 3 patients by computing the point-to-surface mean distance between the landmark points for each vertebra obtained from pre-operative X-rays and the surface of the segmented vertebra. Our study results in a mean error of 3.41 $\pm$ 1.06 mm. Based on qualitative and quantitative results, we conclude that our method is able to obtain a good segmentation and 3D reconstruction of scoliotic spines, all after training from unpaired data in an unsupervised manner.Comment: To appear in the Proceedings of the SPIE Medical Imaging Conference 2021, San Diego, CA. 9 pages, 4 figures in tota

Visual parameter optimisation for biomedical image processing

Background: Biomedical image processing methods require users to optimise input parameters to ensure high quality output. This presents two challenges. First, it is difficult to optimise multiple input parameters for multiple input images. Second, it is difficult to achieve an understanding of underlying algorithms, in particular, relationships between input and output. Results: We present a visualisation method that transforms users’ ability to understand algorithm behaviour by integrating input and output, and by supporting exploration of their relationships. We discuss its application to a colour deconvolution technique for stained histology images and show how it enabled a domain expert to identify suitable parameter values for the deconvolution of two types of images, and metrics to quantify deconvolution performance. It also enabled a breakthrough in understanding by invalidating an underlying assumption about the algorithm. Conclusions: The visualisation method presented here provides analysis capability for multiple inputs and outputs in biomedical image processing that is not supported by previous analysis software. The analysis supported by our method is not feasible with conventional trial-and-error approaches

3D Ultrasound in the Management of Post Hemorrhagic Ventricle Dilatation

Enlargement of the cerebral ventricles is relatively common among extremely preterm neonates born before 28 weeks gestational age. One common cause of ventricle dilatation is post hemorrhagic ventricle dilatation following a bleed in the cerebral ventricles. While many neonates with PHVD will have spontaneous resolution of the condition, severe, persistent PHVD is associated with a greater risk of brain injury and morbidity later in life and left untreated can cause death. The current clinical management strategy consists of daily measurements of head circumference and qualitative interpretation of two-dimensional US images to detect ventricular enlargement and monitoring vital signs for indications increased intracranial pressure (i.e. apnea, bradycardia). Despite the widespread clinical use of these indicators, they do not have the specificity to reliably indicate when an intervention to remove some CSF is required to prevent brain damage. Early recognition of interventional necessity using quantitative measurements could help with the management of the disease, and could lead to better care in the future. Our objective was to develop and validate a three-dimensional ultrasound system for use within an incubator of neonates with PHVD in order to accurately measure the cerebral ventricle volume. This system was validated against known geometric phantoms as well as a custom ventricle-like phantom. Once validated, the system was used in a clinical study of 70 neonates with PHVD to measure the ventricle size. In addition to three-dimensional ultrasound, clinical ultrasound images, and MRIs were attained. Clinical measurements of the ventricles and three-dimensional ultrasound ventricle volumes were used to determine thresholds between neonates with PHVD who did and did not receive interventions based on current clinical management. We determined image based thresholds for intervention for both neonates who will receive an initial intervention, as well as those who will receive multiple interventions. Three-dimensional ultrasound based ventricle volume measurements had high sensitivity and specificity as patients with persistent PHVD have ventricles that increase in size faster than those who undergo resolution. This allowed for delineation between interventional and non-interventional patients within the first week of life. While this is still a small sample size study, these results can give rise to larger studies that would be able to determine if earlier intervention can result in better neurodevelopmental outcomes later in life

Medical Image Segmentation with Deep Learning

Medical imaging is the technique and process of creating visual representations of the body of a patient for clinical analysis and medical intervention. Healthcare professionals rely heavily on medical images and image documentation for proper diagnosis and treatment. However, manual interpretation and analysis of medical images is time-consuming, and inaccurate when the interpreter is not well-trained. Fully automatic segmentation of the region of interest from medical images have been researched for years to enhance the efficiency and accuracy of understanding such images. With the advance of deep learning, various neural network models have gained great success in semantic segmentation and spark research interests in medical image segmentation using deep learning. We propose two convolutional frameworks to segment tissues from different types of medical images. Comprehensive experiments and analyses are conducted on various segmentation neural networks to demonstrate the effectiveness of our methods. Furthermore, datasets built for training our networks and full implementations are published

The inertial properties of the German Shepherd

Previously held under moratorium from 30th November 2016 until 30th November 2021The police service dog has a long history stretching as far back as the 1400’s. One of the most popular dog breeds deployed by both the police and military has been the German Shepherd yet little is known about the morphology or body segment parameters of this breed. Knowledge of these measures is essential for developing biomechanical models that can guide clinicians in developing surgical interventions, injury treatment and prevention procedures. The aim of this thesis was to provide a complete set of body segment parameters and inertial properties for the German Shepherd. In addition, a canine motion capture suit and marker model was proposed for use with this dog population. Morphometric measures and 3-dimensional inertial properties, including mass, centre of mass, moment of inertia and volume, were measured from 17 segments from each of 6 German Shepherd police service dog cadavers. Measurements were performed with frozen segments similar to the procedure on primates described by Reynolds (1974), on humans by Chandler et al. (1975) and on horses by Buchner et al. (1997). Using whole body mass and geometric modelling, multiple linear regression equations were developed from the collected data so that they may be used to estimate segment masses and inertial tensors in living dogs. Using a custom Lycra suit and 44-marker full-body marker set, kinematic data were collected to assess the practicality of the model, to observe the dogs’ acceptance of the motion capture suit and to ensure fore and hind limb flexion/extension angles were comparable to those of other canine studies. Using frozen cadavers, tissue loss was minimal at an average loss of 0.49% of total body mass. Hind limbs, at 6.8% of body mass, were 2.3% heavier than the forelimbs. Of the over 100 morphometric measures analysed, 33 were kept for inclusion in the linear regression equations and joint centre estimations. Analyses of body mass alone, found that, except for the abdominal segment (r = .845, p≤.05), body mass did not correlate well with segmental masses. Similarly for moments of inertia, only the manus and pes produced predictive results using body mass alone. 11 regression equations were developed for predicting segment masses, and 33 equations were developed for predicting moments of inertia about the three primary axes of each segment. Regression correlation analyses were summarized for each segment and a table of normalised average segment masses, centres of mass, radii of gyration and segment densities was produced. Five police service dogs took part in the evaluation of the motion capture suit. Overall the marker set and suit performed well and was well-received by dog/handler teams. The markers took very little time to apply, remained in place for the majority of trials and the suit itself did not visibly affect the dog’s natural movement. An analysis of the kinematic data produced outputs showing characteristic patterns of flexion/extension similar to those found in other canine research. With the development of regression equations for predicting segment mass and moments of inertia combined with the proposed marker model and novel method of marker attachment, inverse dynamic analyses may be applied in future investigations of canine mechanics, potentially guiding surgical procedures, rehabilitation and training for the German Shepherd breed. Key Words: Canine, German Shepherd, morphometry, kinematics, kinetics, inertial properties, body segment parameter, segment model, moment of inertia, mass distribution.The police service dog has a long history stretching as far back as the 1400’s. One of the most popular dog breeds deployed by both the police and military has been the German Shepherd yet little is known about the morphology or body segment parameters of this breed. Knowledge of these measures is essential for developing biomechanical models that can guide clinicians in developing surgical interventions, injury treatment and prevention procedures. The aim of this thesis was to provide a complete set of body segment parameters and inertial properties for the German Shepherd. In addition, a canine motion capture suit and marker model was proposed for use with this dog population. Morphometric measures and 3-dimensional inertial properties, including mass, centre of mass, moment of inertia and volume, were measured from 17 segments from each of 6 German Shepherd police service dog cadavers. Measurements were performed with frozen segments similar to the procedure on primates described by Reynolds (1974), on humans by Chandler et al. (1975) and on horses by Buchner et al. (1997). Using whole body mass and geometric modelling, multiple linear regression equations were developed from the collected data so that they may be used to estimate segment masses and inertial tensors in living dogs. Using a custom Lycra suit and 44-marker full-body marker set, kinematic data were collected to assess the practicality of the model, to observe the dogs’ acceptance of the motion capture suit and to ensure fore and hind limb flexion/extension angles were comparable to those of other canine studies. Using frozen cadavers, tissue loss was minimal at an average loss of 0.49% of total body mass. Hind limbs, at 6.8% of body mass, were 2.3% heavier than the forelimbs. Of the over 100 morphometric measures analysed, 33 were kept for inclusion in the linear regression equations and joint centre estimations. Analyses of body mass alone, found that, except for the abdominal segment (r = .845, p≤.05), body mass did not correlate well with segmental masses. Similarly for moments of inertia, only the manus and pes produced predictive results using body mass alone. 11 regression equations were developed for predicting segment masses, and 33 equations were developed for predicting moments of inertia about the three primary axes of each segment. Regression correlation analyses were summarized for each segment and a table of normalised average segment masses, centres of mass, radii of gyration and segment densities was produced. Five police service dogs took part in the evaluation of the motion capture suit. Overall the marker set and suit performed well and was well-received by dog/handler teams. The markers took very little time to apply, remained in place for the majority of trials and the suit itself did not visibly affect the dog’s natural movement. An analysis of the kinematic data produced outputs showing characteristic patterns of flexion/extension similar to those found in other canine research. With the development of regression equations for predicting segment mass and moments of inertia combined with the proposed marker model and novel method of marker attachment, inverse dynamic analyses may be applied in future investigations of canine mechanics, potentially guiding surgical procedures, rehabilitation and training for the German Shepherd breed. Key Words: Canine, German Shepherd, morphometry, kinematics, kinetics, inertial properties, body segment parameter, segment model, moment of inertia, mass distribution