Multi-modal glioblastoma segmentation: man versus machine

BACKGROUND AND PURPOSE Reproducible segmentation of brain tumors on magnetic resonance images is an important clinical need. This study was designed to evaluate the reliability of a novel fully automated segmentation tool for brain tumor image analysis in comparison to manually defined tumor segmentations. METHODS We prospectively evaluated preoperative MR Images from 25 glioblastoma patients. Two independent expert raters performed manual segmentations. Automatic segmentations were performed using the Brain Tumor Image Analysis software (BraTumIA). In order to study the different tumor compartments, the complete tumor volume TV (enhancing part plus non-enhancing part plus necrotic core of the tumor), the TV+ (TV plus edema) and the contrast enhancing tumor volume CETV were identified. We quantified the overlap between manual and automated segmentation by calculation of diameter measurements as well as the Dice coefficients, the positive predictive values, sensitivity, relative volume error and absolute volume error. RESULTS Comparison of automated versus manual extraction of 2-dimensional diameter measurements showed no significant difference (p = 0.29). Comparison of automated versus manual segmentation of volumetric segmentations showed significant differences for TV+ and TV (p<0.05) but no significant differences for CETV (p>0.05) with regard to the Dice overlap coefficients. Spearman's rank correlation coefficients (ρ) of TV+, TV and CETV showed highly significant correlations between automatic and manual segmentations. Tumor localization did not influence the accuracy of segmentation. CONCLUSIONS In summary, we demonstrated that BraTumIA supports radiologists and clinicians by providing accurate measures of cross-sectional diameter-based tumor extensions. The automated volume measurements were comparable to manual tumor delineation for CETV tumor volumes, and outperformed inter-rater variability for overlap and sensitivity.

Coloured Letters and Numbers (CLaN): A reliable factor-analysis based synaesthesia questionnaire

Synaesthesia is a heterogeneous phenomenon, even when considering one particular sub-type. The purpose of this study was to design a reliable and valid questionnaire for grapheme-colour synaesthesia that captures this heterogeneity. By the means of a large sample of 628 synaesthetes and a factor analysis, we created the Coloured Letters and Numbers (CLaN) questionnaire with 16 items loading on 4 different factors (i.e., localisation, automaticity/attention, deliberate use, and longitudinal changes). These factors were externally validated with tests which are widely used in the field of synaesthesia research. The questionnaire showed good test–retest reliability and construct validity (i.e., internally and externally). Our findings are discussed in the light of current theories and new ideas in synaesthesia research. More generally, the questionnaire is a useful tool which can be widely used in synaesthesia research to reveal the influence of individual differences on various performance measures and will be useful in generating new hypotheses.

The brown coat colour of Coppernecked goats is associated with a non-synonymous variant at the TYRP1 locus on chromosome 8

The recent development of a goat SNP genotyping microarray enables genome-wide association studies in this important livestock species. We investigated the genetic basis of the black and brown coat colour in Valais Blacknecked and Coppernecked goats. A genome-wide association analysis using goat SNP50 BeadChip genotypes of 22 cases and 23 controls allowed us to map the locus for the brown coat colour to goat chromosome 8. The TYRP1 gene is located within the associated chromosomal region, and TYRP1 variants cause similar coat colour phenotypes in different species. We thus considered TYRP1 as a strong positional and functional candidate. We resequenced the caprine TYRP1 gene by Sanger and Illumina sequencing and identified two non-synonymous variants, p.Ile478Thr and p.Gly496Asp, that might have a functional impact on the TYRP1 protein. However, based on the obtained pedigree and genotype data, the brown coat colour in these goats is not due to a single recessive loss-of-function allele. Surprisingly, the genotype distribution and the pedigree data suggest that the (496) Asp allele might possibly act in a dominant manner. The (496) Asp allele was present in 77 of 81 investigated Coppernecked goats and did not occur in black goats. This strongly suggests heterogeneity underlying the brown coat colour in Coppernecked goats. Functional experiments or targeted matings will be required to verify the unexpected preliminary findings.

Multi-colour FISH in oesophageal adenocarcinoma-predictors of prognosis independent of stage and grade.

BACKGROUND Oesophageal adenocarcinoma or Barrett's adenocarcinoma (EAC) is increasing in incidence and stratification of prognosis might improve disease management. Multi-colour fluorescence in situ hybridisation (FISH) investigating ERBB2, MYC, CDKN2A and ZNF217 has recently shown promising results for the diagnosis of dysplasia and cancer using cytological samples. METHODS To identify markers of prognosis we targeted four selected gene loci using multi-colour FISH applied to a tissue microarray containing 130 EAC samples. Prognostic predictors (P1, P2, P3) based on genomic copy numbers of the four loci were statistically assessed to stratify patients according to overall survival in combination with clinical data. RESULTS The best stratification into favourable and unfavourable prognoses was shown by P1, percentage of cells with less than two ZNF217 signals; P2, percentage of cells with fewer ERBB2- than ZNF217 signals; and P3, overall ratio of ERBB2-/ZNF217 signals. Median survival times for P1 were 32 vs 73 months, 28 vs 73 months for P2; and 27 vs 65 months for P3. Regarding each tumour grade P2 subdivided patients into distinct prognostic groups independently within each grade, with different median survival times of at least 35 months. CONCLUSIONS Cell signal number of the ERBB2 and ZNF217 loci showed independence from tumour stage and differentiation grade. The prognostic value of multi-colour FISH-assays is applicable to EAC and is superior to single markers.

Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A novel Statistical Shape Model for treatment planning of Retinoblastoma

Purpose: Proper delineation of ocular anatomy in 3D imaging is a big challenge, particularly when developing treatment plans for ocular diseases. Magnetic Resonance Imaging (MRI) is nowadays utilized in clinical practice for the diagnosis confirmation and treatment planning of retinoblastoma in infants, where it serves as a source of information, complementary to the Fundus or Ultrasound imaging. Here we present a framework to fully automatically segment the eye anatomy in the MRI based on 3D Active Shape Models (ASM), we validate the results and present a proof of concept to automatically segment pathological eyes. Material and Methods: Manual and automatic segmentation were performed on 24 images of healthy children eyes (3.29±2.15 years). Imaging was performed using a 3T MRI scanner. The ASM comprises the lens, the vitreous humor, the sclera and the cornea. The model was fitted by first automatically detecting the position of the eye center, the lens and the optic nerve, then aligning the model and fitting it to the patient. We validated our segmentation method using a leave-one-out cross validation. The segmentation results were evaluated by measuring the overlap using the Dice Similarity Coefficient (DSC) and the mean distance error. Results: We obtained a DSC of 94.90±2.12% for the sclera and the cornea, 94.72±1.89% for the vitreous humor and 85.16±4.91% for the lens. The mean distance error was 0.26±0.09mm. The entire process took 14s on average per eye. Conclusion: We provide a reliable and accurate tool that enables clinicians to automatically segment the sclera, the cornea, the vitreous humor and the lens using MRI. We additionally present a proof of concept for fully automatically segmenting pathological eyes. This tool reduces the time needed for eye shape delineation and thus can help clinicians when planning eye treatment and confirming the extent of the tumor.

Volumetric analysis of lung nodules in computed tomography (CT): comparison of two different segmentation algorithm softwares and two different reconstruction filters on automated volume calculation.

BACKGROUND A precise detection of volume change allows for better estimating the biological behavior of the lung nodules. Postprocessing tools with automated detection, segmentation, and volumetric analysis of lung nodules may expedite radiological processes and give additional confidence to the radiologists. PURPOSE To compare two different postprocessing software algorithms (LMS Lung, Median Technologies; LungCARE®, Siemens) in CT volumetric measurement and to analyze the effect of soft (B30) and hard reconstruction filter (B70) on automated volume measurement. MATERIAL AND METHODS Between January 2010 and April 2010, 45 patients with a total of 113 pulmonary nodules were included. The CT exam was performed on a 64-row multidetector CT scanner (Somatom Sensation, Siemens, Erlangen, Germany) with the following parameters: collimation, 24x1.2 mm; pitch, 1.15; voltage, 120 kVp; reference tube current-time, 100 mAs. Automated volumetric measurement of each lung nodule was performed with the two different postprocessing algorithms based on two reconstruction filters (B30 and B70). The average relative volume measurement difference (VME%) and the limits of agreement between two methods were used for comparison. RESULTS At soft reconstruction filters the LMS system produced mean nodule volumes that were 34.1% (P < 0.0001) larger than those by LungCARE® system. The VME% was 42.2% with a limit of agreement between -53.9% and 138.4%.The volume measurement with soft filters (B30) was significantly larger than with hard filters (B70); 11.2% for LMS and 1.6% for LungCARE®, respectively (both with P < 0.05). LMS measured greater volumes with both filters, 13.6% for soft and 3.8% for hard filters, respectively (P < 0.01 and P > 0.05). CONCLUSION There is a substantial inter-software (LMS/LungCARE®) as well as intra-software variability (B30/B70) in lung nodule volume measurement; therefore, it is mandatory to use the same equipment with the same reconstruction filter for the follow-up of lung nodule volume.

Automated 3D Lumbar Intervertebral Disc Segmentation from MRI Data Sets

This paper proposed an automated 3D lumbar intervertebral disc (IVD) segmentation strategy from MRI data. Starting from two user supplied landmarks, the geometrical parameters of all lumbar vertebral bodies and intervertebral discs are automatically extracted from a mid-sagittal slice using a graphical model based approach. After that, a three-dimensional (3D) variable-radius soft tube model of the lumbar spine column is built to guide the 3D disc segmentation. The disc segmentation is achieved as a multi-kernel diffeomorphic registration between a 3D template of the disc and the observed MRI data. Experiments on 15 patient data sets showed the robustness and the accuracy of the proposed algorithm.

Fully automatic segmentation of AP pelvis X-rays via random forest regression with efficient feature selection and hierarchical sparse shape composition

In clinical practice, traditional X-ray radiography is widely used, and knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic approach for landmark detection and shape segmentation of both pelvis and femur in conventional AP X-ray images. Our approach is based on the framework of landmark detection via Random Forest (RF) regression and shape regularization via hierarchical sparse shape composition. We propose a visual feature FL-HoG (Flexible- Level Histogram of Oriented Gradients) and a feature selection algorithm based on trace radio optimization to improve the robustness and the efficacy of RF-based landmark detection. The landmark detection result is then used in a hierarchical sparse shape composition framework for shape regularization. Finally, the extracted shape contour is fine-tuned by a post-processing step based on low level image features. The experimental results demonstrate that our feature selection algorithm reduces the feature dimension in a factor of 40 and improves both training and test efficiency. Further experiments conducted on 436 clinical AP pelvis X-rays show that our approach achieves an average point-to-curve error around 1.2 mm for femur and 1.9 mm for pelvis.

A complete-pelvis segmentation framework for image-free total hip arthroplasty (THA): methodology and clinical study

Background Complete-pelvis segmentation in antero-posterior pelvic radiographs is required to create a patient-specific three-dimensional pelvis model for surgical planning and postoperative assessment in image-free navigation of total hip arthroplasty. Methods A fast and robust framework for accurately segmenting the complete pelvis is presented, consisting of two consecutive modules. In the first module, a three-stage method was developed to delineate the left hemipelvis based on statistical appearance and shape models. To handle complex pelvic structures, anatomy-specific information processing techniques were employed. As the input to the second module, the delineated left hemi-pelvis was then reflected about an estimated symmetry line of the radiograph to initialize the right hemi-pelvis segmentation. The right hemi-pelvis was segmented by the same three-stage method, Results Two experiments conducted on respectively 143 and 40 AP radiographs demonstrated a mean segmentation accuracy of 1.61±0.68 mm. A clinical study to investigate the postoperative assessment of acetabular cup orientations based on the proposed framework revealed an average accuracy of 1.2°±0.9° and 1.6°±1.4° for anteversion and inclination, respectively. Delineation of each radiograph costs less than one minute. Conclusions Despite further validation needed, the preliminary results implied the underlying clinical applicability of the proposed framework for image-free THA.

Body weight estimation based on postmortem CT data-validation of a multiplication factor

Postmortem computed tomography (pmCT) is increasingly applied in forensic medicine as a documentation and diagnostic tool. The present study investigated if pmCT data can be used to estimate the corpse weight. In 50 forensic cases, pmCT examinations were performed prior to autopsy and the pmCT data were used to determine the body volume using an automated segmentation tool. PmCT was performed within 48 h postmortem. The body weights assessed prior to autopsy and the body volumes assessed using the pmCT data were used to calculate individual multiplication factors. The mean postmortem multiplication factor for the study cases was 1.07 g/ml. Using this factor, the body weight may be estimated retrospectively when necessary. Severe artifact causing foreign bodies within the corpses limit the use of pmCT data for body weight estimations.

Fully Automatic Segmentation of Hip CT Images

Automatic segmentation of the hip joint with pelvis and proximal femur surfaces from CT images is essential for orthopedic diagnosis and surgery. It remains challenging due to the narrowness of hip joint space, where the adjacent surfaces of acetabulum and femoral head are hardly distinguished from each other. This chapter presents a fully automatic method to segment pelvic and proximal femoral surfaces from hip CT images. A coarse-to-fine strategy was proposed to combine multi-atlas segmentation with graph-based surface detection. The multi-atlas segmentation step seeks to coarsely extract the entire hip joint region. It uses automatically detected anatomical landmarks to initialize and select the atlas and accelerate the segmentation. The graph based surface detection is to refine the coarsely segmented hip joint region. It aims at completely and efficiently separate the adjacent surfaces of the acetabulum and the femoral head while preserving the hip joint structure. The proposed strategy was evaluated on 30 hip CT images and provided an average accuracy of 0.55, 0.54, and 0.50 mm for segmenting the pelvis, the left and right proximal femurs, respectively.

Automated 3D Lumbar Intervertebral Disc Segmentation from MRI Data Sets

This paper proposed an automated three-dimensional (3D) lumbar intervertebral disc (IVD) segmentation strategy from Magnetic Resonance Imaging (MRI) data. Starting from two user supplied landmarks, the geometrical parameters of all lumbar vertebral bodies and intervertebral discs are automatically extracted from a mid-sagittal slice using a graphical model based template matching approach. Based on the estimated two-dimensional (2D) geometrical parameters, a 3D variable-radius soft tube model of the lumbar spine column is built by model fitting to the 3D data volume. Taking the geometrical information from the 3D lumbar spine column as constraints and segmentation initialization, the disc segmentation is achieved by a multi-kernel diffeomorphic registration between a 3D template of the disc and the observed MRI data. Experiments on 15 patient data sets showed the robustness and the accuracy of the proposed algorithm.

Diagnosing synaesthesia with online colour pickers: Maximising sensitivity and specificity

The most commonly used method for formally assessing grapheme-colour synaesthesia (i.e., experiencing colours in response to letter and/or number stimuli) involves selecting colours from a large colour palette on several occasions and measuring consistency of the colours selected. However, the ability to diagnose synaesthesia using this method depends on several factors that have not been directly contrasted. These include the type of colour space used (e.g., RGB, HSV, CIELUV, CIELAB) and different measures of consistency (e.g., city block and Euclidean distance in colour space). This study aims to find the most reliable way of diagnosing grapheme-colour synaesthesia based on maximising sensitivity (i.e., ability of a test to identify true synaesthetes) and specificity (i.e., ability of a test to identify true non-synaesthetes). We show, applying ROC (Receiver Operating Characteristics) to binary classification of a large sample of self-declared synaesthetes and non-synaesthetes, that the consistency criterion (i.e., cut-off value) for diagnosing synaesthesia is considerably higher than the current standard in the field. We also show that methods based on perceptual CIELUV and CIELAB colour models (rather than RGB and HSV colour representations) and Euclidean distances offer an even greater sensitivity and specificity than most currently used measures. Together, these findings offer improved heuristics for the behavioural assessment of grapheme-colour synaesthesia.

MASCG: Multi-Atlas Segmentation Constrained Graph method for accurate segmentation of hip CT images

This paper addresses the issue of fully automatic segmentation of a hip CT image with the goal to preserve the joint structure for clinical applications in hip disease diagnosis and treatment. For this purpose, we propose a Multi-Atlas Segmentation Constrained Graph (MASCG) method. The MASCG method uses multi-atlas based mesh fusion results to initialize a bone sheetness based multi-label graph cut for an accurate hip CT segmentation which has the inherent advantage of automatic separation of the pelvic region from the bilateral proximal femoral regions. We then introduce a graph cut constrained graph search algorithm to further improve the segmentation accuracy around the bilateral hip joint regions. Taking manual segmentation as the ground truth, we evaluated the present approach on 30 hip CT images (60 hips) with a 15-fold cross validation. When the present approach was compared to manual segmentation, an average surface distance error of 0.30 mm, 0.29 mm, and 0.30 mm was found for the pelvis, the left proximal femur, and the right proximal femur, respectively. A further look at the bilateral hip joint regions demonstrated an average surface distance error of 0.16 mm, 0.21 mm and 0.20 mm for the acetabulum, the left femoral head, and the right femoral head, respectively.

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference. Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires. Methods: The study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB. Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use. Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.