Hierarchical Framework for Automatic Pancreas Segmentation in MRI Using Continuous Max-flow and Min-Cuts Approach

Accurate, automatic and robust segmentation of the pancreas in medical image scans remains a challenging but important prerequisite for computer-aided diagnosis (CADx). This paper presents a tool for automatic pancreas segmentation in magnetic resonance imaging (MRI) scans. Proposed is a framework that employs a hierarchical pooling of information as follows: identify major pancreas region and apply contrast enhancement to differentiate between pancreatic and surrounding tissue; perform 3D segmentation by employing continuous max-flow and min-cuts approach, structured forest edge detection, and a training dataset of annotated pancreata; eliminate non-pancreatic contours from resultant segmentation via morphological operations on area, curvature and position between distinct contours. The proposed method is evaluated on a dataset of 20 MRI volumes, achieving a mean Dice Similarity coefficient of 75.5 ± 7.0% and a mean Jaccard Index coefficient of 61.2 ± 9.2%

Normalization of Poincaré singularities via variation of constants

We present a geometric proof of the Poincaré-Dulac Normalization Theorem for analytic vector fields with singularities of Poincaré type. Our approach allows us to relate the size of the convergence domain of the linearizing transformation to the geometry of the complex foliation associated to the vector field. A similar construction is considered in the case of linearization of maps in a neighborhood of a hyperbolic fixed point

Evaluation of global impact models' ability to reproduce runoff characteristics over the central United States

The central United States experiences a wide array of hydrological extremes, with the 1993, 2008, 2013, and 2014 flooding events and the 1988 and 2012 droughts representing some of the most recent extremes, and is an area where water availability is critical for agricultural production. This study aims to evaluate the ability of a set of global impact models (GIMs) from the Water Model Intercomparison Project to reproduce the regional hydrology of the central United States for the period 1963–2001. Hydrological indices describing annual daily maximum, medium and minimum flow, and their timing are extracted from both modeled daily runoff data by nine GIMs and from observed daily streamflow measured at 252 river gauges. We compare trend patterns for these indices, and their ability to capture runoff volume differences for the 1988 drought and 1993 flood. In addition, we use a subset of 128 gauges and corresponding grid cells to perform a detailed evaluation of the models on a gauge-to-grid cell basis. Results indicate that these GIMs capture the overall trends in high, medium, and low flows well. However, the models differ from observations with respect to the timing of high and medium flows. More specifically, GIMs that only include water balance tend to be closer to the observations than GIMs that also include the energy balance. In general, as it would be expected, the performance of the GIMs is the best when describing medium flows, as opposed to the two ends of the runoff spectrum. With regards to low flows, some of the GIMs have considerably large pools of zeros or low values in their time series, undermining their ability in capturing low flow characteristics and weakening the ensemble's output. Overall, this study provides a valuable examination of the capability of GIMs to reproduce observed regional hydrology over a range of quantities for the central United States

A Framework for Morphological Feature Extraction of Organs from MR Images for Detection and Classification of Abnormalities

In clinical practice, a misdiagnosis can lead to incorrect or delayed treatment, and in some cases, no treatment at all; consequently, the condition of a patient may worsen to varying degrees, in some cases proving fatal. The accurate 3D reconstruction of organs, which is a pioneering tool of medical image computing (MIC) technology, plays a key role in computer aided diagnosis (CADx), thereby enabling medical professionals to perform enhanced analysis on a region of interest. From here, the shape and structure of the organ coupled with measurements of its volume and curvature can provide significant guidance towards establishing the severity of a disorder or abnormality, consequently supporting improved diagnosis and treatment planning. Moreover, the classification and stratification of organ abnormalities is widely utilised within biomedical, forensic and MIC research for exploring and investigating organ deformations following injury, illness or trauma. This paper presents a tool that calculates, classifies and analyses pancreatic volume and curvature following their 3D reconstruction. Magnetic resonance imaging (MRI) volumes of 115 adult patients are evaluated in order to examine a correlation between these two variables. Such a tool can be utilised in the scope of much greater research and investigation. It can also be incorporated into the development of effective medical image analysis software application in the stratification of subjects and targeting of therapies

Lagrange's discrete model of the wave equation in dimension greater than one

A celebrated theorem of Lagrange states that a solution of the wave equation with one-dimensional space variable is the uniform limit, as N tends to infinity, of a second order ODE obtained from a mechanical model discretizing a string as N identical harmonic oscillators. Answering to a question posed by G. Gallavotti we generalize this result to the case of any space dimension

AI Driven IoT Web-Based Application for Automatic Segmentation and Reconstruction of Abdominal Organs from Medical Images

Medical imaging technology has rapidly advanced in the last few decades, providing detailed images of the human body. The accurate analysis of these images and the segmentation of anatomical structures can produce significant morphological information, provide additional guidance toward subject stratification after diagnosis or before a clinical trial, and help predict a medical condition. Usually, medical scans are manually segmented by expert operators, such as radiologists and radiographers, which is complex, time-consuming and prone to inter-observer variability. A system that generates automatic, accurate quantitative organ segmentation on a large scale could deliver a clinical impact, supporting current investigations in subjects with medical conditions and aiding early diagnosis and treatment planning. This paper proposes a web-based application that automatically segments multiple abdominal organs and muscle, produces respective 3D reconstructions and extracts valuable biomarkers using a deep learning backend engine. Furthermore, it is possible to upload image data and access the medical image segmentation tool without installation using any device connected to the Internet. The final aim is to deliver a web- based image-processing service that clinical experts, researchers and users can seamlessly access through IoT devices without requiring knowledge of the underpinning technology