A COMPUTATIONAL PIPELINE FOR MCI DETECTION FROM HETEROGENEOUS BRAIN IMAGES

The aging population has increased the importance of identifying and understanding mild cognitive impairment (MCI), particularly given that 6 - 15 % of MCI cases convert to Alzheimer\u27s disease (AD) each year. The early identification of MCI has the potential for timely therapeutic interventions that would limit the advancement of MCI to AD. However, it is difficult to identify MCI-related pathology based on visual inspection because these changes in brain morphology are subtle and spatially distributed. Therefore, reliable and automated methods to identify subtle changes in morphological characteristics of MCI would aid in the identification and understanding of MCI. Meanwhile, usability becomes a major limitation in the development of clinically applicable classifiers. Furthermore, subject privacy is an additional issue in the usage of human brain images. To address the critical need, a complete computer aided diagnosis (CAD) system for automated detection of MCI from heterogeneous brain images is developed. This system provides functions for image processing, classification of MCI subjects from control, visualization of affected regions of interest (ROIs), data sharing among different research sites, and knowledge sharing through image annotation

Wize Mirror - a smart, multisensory cardio-metabolic risk monitoring system

In the recent years personal health monitoring systems have been gaining popularity, both as a result of the pull from the general population, keen to improve well-being and early detection of possibly serious health conditions and the push from the industry eager to translate the current significant progress in computer vision and machine learning into commercial products. One of such systems is the Wize Mirror, built as a result of the FP7 funded SEMEOTICONS (SEMEiotic Oriented Technology for Individuals CardiOmetabolic risk self-assessmeNt and Self-monitoring) project. The project aims to translate the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, multispectral images, and 3D scans of the face. The multisensory platform, being developed as the result of that project, in the form of a smart mirror, looks for signs related to cardio-metabolic risks. The goal is to enable users to self-monitor their well-being status over time and improve their life-style via tailored user guidance. This paper is focused on the description of the part of that system, utilising computer vision and machine learning techniques to perform 3D morphological analysis of the face and recognition of psycho-somatic status both linked with cardio-metabolic risks. The paper describes the concepts, methods and the developed implementations as well as reports on the results obtained on both real and synthetic datasets

Tissue Parameter Mapping in Children with Fetal Alcohol Spectrum Disorders

Background: Fetal alcohol spectrum disorders (FASD), which are caused by prenatal alcohol exposure (PAE), affects people around the world. Certain communities in South Africa have among the highest reported incidences of fetal alcohol syndrome (FAS) in the world. Although PAE-related brain alterations have been widely documented, the mechanisms whereby alcohol affects the brain are not clearly understood. MRI relaxation parameters T1, T2, T2* and proton density (PD), are basic tissue properties that reflect the underlying biology. The present study aims to advance our understanding of how PAE alters the microstructural properties of tissue by examining PAE-related changes in these tissue parameters in adolescents with FASD. Methods: The final sample used in this study consisted of 53 children from a previously studied longitudinal cohort (Jacobson et al., 2008) and 12 additionally recruited subjects. Of the 65 participants, 18 were diagnosed with FAS or partial FAS (PFAS) and made up the FAS/PFAS group, 18 were diagnosed as heavily exposed non-syndromal (HE) and 29 were age matched controls. Subjects were scanned at the Cape Universities Body Imaging Centre (CUBIC) located at Groote Schuur Hospital on a 3T Siemens Skyra MRI. Structural images were obtained using the MEMPRAGE sequence. From these images T1, T2, T2* and PD parameter maps were constructed and segmented into 43 regions of interest (ROI) using Freesurfer, FSL and AFNI. Linear regression analyses were used to analyse group differences as well as correlations between parameter values and the amount of alcohol the mother consumed during pregnancy. Results: Significant T1 differences were found in the caudate, cerebellar cortex, hippocampus, accumbens, putamen, choroid plexus, ventral diencephalon (DC), right vessel and ventricles. Significant T2 differences were found in the caudate, brain stem, corpus callosum (CC), amygdala, cerebral cortex, choroid plexus, vessels and ventricles. Significant T2* differences were found in the cerebellar cortex, optic chiasm and ventricles. Significant PD differences were found in the hippocampus and left lateral ventricle. The exploratory nature of this study resulted in none of the results surviving FDR correction for multiple comparisons. Conclusions: Overall, our findings point to regional PAE-related increases in water content and cellular and molecular changes in underlying tissue of the anatomical structure. Exceptions were the right cerebral cortex, brain stem, hippocampus, amygdala and ventral diencephalon where our findings point to less free water and increased cell density, and cerebellar cortex where simultaneous reductions in T1 and T2* suggest the possibility of increased iron content. In highly myelinated white matter structures, such as the CC and optic chiasm, our results point to PAErelated demyelination, and possibly increased iron. These findings extend previous knowledge of effects of PAE and demonstrate that tissues are affected at a microstructural level

3T MRI-radiomic approach to predict for lymph node status in breast cancer patients

Simple SummaryBreast cancer is the most common cancer in women worldwide. The axillary lymph node status is one of the main prognostic factors. Currently, the methods to define the lymph node status are invasive and not without sequelae (from biopsy to lymphadenectomy). Radiomics is a new tool, and highly varied, but with high potential that has already shown excellent results in numerous fields of application. In our study, we have developed a classifier validated on a relatively large number of patients, which is able to predict lymph node status using a combination of patients clinical features, primary breast cancer histological features and radiomics features based on 3 Tesla post contrast-MR images. This approach can accurately select breast cancer patients who may avoid unnecessary biopsy and lymphadenectomy in a non-invasive way.Background: axillary lymph node (LN) status is one of the main breast cancer prognostic factors and it is currently defined by invasive procedures. The aim of this study is to predict LN metastasis combining MRI radiomics features with primary breast tumor histological features and patients' clinical data. Methods: 99 lesions on pre-treatment contrasted 3T-MRI (DCE). All patients had a histologically proven invasive breast cancer and defined LN status. Patients' clinical data and tumor histological analysis were previously collected. For each tumor lesion, a semi-automatic segmentation was performed, using the second phase of DCE-MRI. Each segmentation was optimized using a convex-hull algorithm. In addition to the 14 semantics features and a feature ROI volume/convex-hull volume, 242 other quantitative features were extracted. A wrapper selection method selected the 15 most prognostic features (14 quantitative, 1 semantic), used to train the final learning model. The classifier used was the Random Forest. Results: the AUC-classifier was 0.856 (label = positive or negative). The contribution of each feature group was lower performance than the full signature. Conclusions: the combination of patient clinical, histological and radiomics features of primary breast cancer can accurately predict LN status in a non-invasive way

The morphology of the intraparietal sulcus in children prenatally exposed to alcohol in a sample of children from the Western Cape, South Africa and its potential relationship with number processing

The intraparietal sulcus (IPS) is a prominent feature in the parietal lobe and extends posteriorly from the postcentral sulcus through the parietal lobe to end in the occipital. It is involved in visuospatial functions and is known to play a critical role in number processing. Fetal alcohol spectrum disorders (FASD) result from prenatal exposure to alcohol and are particularly prevalent in the Western Cape region of South Africa. Arithmetic is a domain of cognitive function that is particularly sensitive to prenatal alcohol exposure, and effects on arithmetic remain significant after controlling for lower IQ. Magnetic resonance imaging (MRI) was used to investigate the morphology of the IPS and whether this morphology had a relation to the number processing abilities of children prenatally exposed to alcohol in a Western Cape community. Participants were 9 to 14-year-old children from the same community in Cape Town, South Africa, who formed part of a study aimed at investigating the effects of prenatal alcohol exposure (PAE) on brain structure and function particularly during number processing. Mothers were interviewed regarding alcohol consumption during pregnancy using a timeline follow-back approach. The first analysis included designing a protocol for manually parcellating the IPS into two regions of interest (ROI): the medial wall (MIPS) and the lateral wall (LIPS) respectively. The neuroimaging program MultiTracer was used for the manual tracing and to calculate the volume of the cortex of both the MIPS and LIPS. The purpose of this first analysis was to examine the effects of PAE on IPS volume and asymmetry using manual tracing, the relation between IPS volume and number processing performance, and potential moderation by PAE of the relation between IPS volume and number processing performance. Results indicated that when comparing the FAS/PFAS (Fetal Alcohol Syndrome/Partial FAS) children to the controls, PAE had an effect on the left LIPS and higher arithmetic scores were associated with larger bilateral MIPS volumes suggesting that the effect of PAE on math may not be moderated by IPS volume. The left LIPS was significantly smaller in FAS/PFAS individuals when compared by FASD diagnosis, and this remained a trend after controlling for potential confounders. In the second analysis, the automated neuroimaging software program FreeSurfer was used to parcellate the IPS. These volumes were then compared with our previously manually traced volumes. Intra-rater reliability testing was statistically significant for consistency and absolute agreement indicating good retraceability of the designed protocol for manual tracing. Both left and right IPS volumes were significantly larger with the manually traced method compared to automated tracing. The manually traced left IPS yielded stronger results when comparing volumes by diagnostic groups, conversely the automated volumes showed stronger associations with alcohol measures. A possible explanation is that FreeSurfer parcellated the IPS differently to our protocol and does not take into account the extensive variability of the morphology of the sulcus. BrainVoyager QX, another neuroimaging software program was used in the third analysis when looking at the BOLD fMRI data of the participants. For this analysis, the manually traced MIPS and LIPS were subdivided into five ROI's for the left and right hemispheres respectively: (1) the superior MIPS, (2) the medial branch of the MIPS, (3) the inferior MIPS, (4) the superior LIPS, and (5) the inferior LIPS. The percent signal change were calculated for each participant for the proximity judgement (PJ) tasks they performed inside the scanner. Associations of the percent signal change of the ROI's of the PAE children with absolute alcohol per occasion (oz) were all significant even after controlling for IQ except the left inferior LIPS, supporting what is found in the literature. The current findings, in agreement with previous studies, demonstrate that PAE is associated with both structural and functional changes in the brain. While the morphology of the IPS may not moderate the effects of PAE on arithmetic function, some cortical volumes within the IPS were sensitive to PAE. Moreover, altered activation of the IPS in the performance of magnitude comparison tasks was strongly associated with PAE. The IPS is an extremely variable structure whose anatomy is often misunderstood, which emphasises the importance of anatomical knowledge for imaging studies. Future research will refine the protocol for manual tracing of the IPS, which may lead to greater understanding of the functions of the different areas. It is to be hoped that these findings will give more insight into understanding the functioning of children and adults with FASDs and contribute to more effective therapeutic interventions for these individuals

Automatic Segmentation of Mandible from Conventional Methods to Deep Learning-A Review

Medical imaging techniques, such as (cone beam) computed tomography and magnetic resonance imaging, have proven to be a valuable component for oral and maxillofacial surgery (OMFS). Accurate segmentation of the mandible from head and neck (H&N) scans is an important step in order to build a personalized 3D digital mandible model for 3D printing and treatment planning of OMFS. Segmented mandible structures are used to effectively visualize the mandible volumes and to evaluate particular mandible properties quantitatively. However, mandible segmentation is always challenging for both clinicians and researchers, due to complex structures and higher attenuation materials, such as teeth (filling) or metal implants that easily lead to high noise and strong artifacts during scanning. Moreover, the size and shape of the mandible vary to a large extent between individuals. Therefore, mandible segmentation is a tedious and time-consuming task and requires adequate training to be performed properly. With the advancement of computer vision approaches, researchers have developed several algorithms to automatically segment the mandible during the last two decades. The objective of this review was to present the available fully (semi)automatic segmentation methods of the mandible published in different scientific articles. This review provides a vivid description of the scientific advancements to clinicians and researchers in this field to help develop novel automatic methods for clinical applications