Predicting the available space for liver transplantation in cirrhotic patients: a computed tomography-based volumetric study.

BACKGROUND: Anthropometric parameters (weight, height) are usually used for quick matching between two individuals (donor and recipient) in liver transplantation (LT). This study aimed to evaluate clinical factors influencing the overall available space for implanting a liver graft in cirrhotic patients. METHODS: In a cohort of 275 cirrhotic patients undergoing LT, we calculated the liver volume (LV), cavity volume (CV), which is considered the additional space between the liver and the right hypocondrium, and the overall volume (OV = LV + CV) using a computed tomography (CT)-based volumetric system. We then chose the formula based on anthropometric parameters that showed the best predictive value for LV. This formula was used to predict the OV in the same population. Factors influencing OV variations were identified by multivariable logistic analysis. RESULTS: The Hashimoto formula (961.3 × BSA_D-404.8) yielded the lowest median absolute percentage error (21.7%) in predicting the LV. The median LV was 1531 ml. One-hundred eighty-five patients (67.2%) had a median CV of 1156 ml (range: 70-7006), and the median OV was 2240 ml (range: 592-8537). Forty-nine patients (17%) had an OV lower than that predicted by the Hashimoto formula. Independent factors influencing the OV included the number of portosystemic shunts, right anteroposterior abdominal diameter, model for end-stage liver disease (MELD) score > 25, high albumin value, and BMI > 30. CONCLUSIONS: Additional anthropometric characteristics (right anteroposterior diameter, body mass index) clinical (number of portosystemic shunts), and biological (MELD, albumin) factors might influence the overall volume available for liver graft implantation. Knowledge of these factors might be helpful during the donor-recipient matching.

Analysis of factors associated with discrepancies between predicted and observed liver weight in liver transplantation.

BACKGROUND: Even using predictive formulas based on anthropometrics in about 30% of subjects, liver weight (LW) cannot be predicted with a ≤20% margin of error. We aimed to identify factors associated with discrepancies between predicted and observed LW. METHODS: In 500 consecutive liver grafts, we tested LW predictive performance using 17 formulas based on anthropometric characteristics. Hashimoto's formula (961.3 × BSA_D-404.8) was associated with the lowest mean absolute error and used to predict LW for the entire cohort. Clinical factors associated with a ≥20% margin of error were identified in a multivariable analysis after propensity score matching (PSM) of donors with similar anthropometric characteristics. RESULTS: The total LW was underestimated with a ≥20% margin of error in 53/500 (10.6%) donors and overestimated in 62/500 (12%) donors. After PSM analysis, ages ≥ 65, (OR = 3.21; CI95% = 1.63-6.31; P = .0007), age ≤ 30 years, (OR = 2.92; CI95% = 1.15-7.40; P = .02), and elevated gamma-glutamyltransferase (GGT) levels (OR = 0.98; CI95% = 0.97-0.99; P = .006), influenced the risk of LW overestimation. Age ≥ 65 years, (OR = 5.98; CI95% = 2.28-15.6; P = .0002), intensive care unit (ICU) stay with ventilation > 7 days, (OR = 0.32; CI95% = 0.12-0.85; P = .02) and waist circumference increase (OR = 1.02; CI95% = 1.00-1.04; P = .04) were factors associated with LW underestimation. CONCLUSIONS: Increased waist circumference, age, prolonged ICU stay with ventilation, elevated GGT were associated with an increase in the margin of error in LW prediction. These factors and anthropometric characteristics could help transplant surgeons during the donor-recipient matching process.

Large-for-Size Orthotopic Liver Transplantation: a Systematic Review of Definitions, Outcomes, and Solutions.

BACKGROUND: We systematically reviewed the literature on definitions and outcomes of large-for-size (LFS) syndrome in orthotopic liver transplantation (LT). METHODS: This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Cochrane Library, PubMed, and Embase were searched (January 1990-January 2019) for studies reporting LFS in LT. Primary outcomes were definitions and mortality of LFS LT. RESULTS: Eleven studies reporting patients with LFS LT were identified. Four different formulas (graft-to-recipient weight ratio (GRWR), body surface area index (BSAi), donor standardized total liver volume (sTLV)-to-recipient sTLV ratio, and graft weight/right anteroposterior distance (RAP) ratio) with their critical thresholds were found. There were 81 patients (54% women) with a median weight and height of 62.5 kg (range, 40-105 kg) and 165 cm (range, 145-180 cm). The median graft weight was 1772 g (range, 1290-2400 g), and the median GWRW was 2.77% (range, 2.1-4.00%). Graft venous outflow obstruction was described in seven patients (8.6%). At the time of LT, fascial closure was not achieved in 24 patients (29.6%) and the graft size was reduced by a liver resection in three patients (3.7%). Thirteen deaths (16%) were reported in the first 90 postoperative days with two patients undergoing re-transplant. CONCLUSIONS: LFS LT remains heterogeneously defined but characterized by high mortality rates despite the use of tailored surgical solutions (graft reduction and open abdomen). A composite definition is proposed in order to better describe LFS clinical syndrome.

n D Variational Restoration of Curvilinear Structures With Prior-Based Directional Regularization.

Curvilinear structure restoration in image processing procedures is a difficult task, which can be compounded when these structures are thin, i.e., when their smallest dimension is close to the resolution of the sensor. Many recent restoration methods involve considering a local gradient-based regularization term as prior, assuming gradient sparsity. An isotropic gradient operator is typically not suitable for thin curvilinear structures, since gradients are not sparse for these. In this paper, we propose a mixed gradient operator that combines a standard gradient in the isotropic image regions, and a directional gradient in the regions where specific orientations are likely. In particular, such information can be provided by curvilinear structure detectors (e.g., RORPO or Frangi filters). Our proposed mixed gradient operator, that can be viewed as a companion tool of such detectors, is proposed in a discrete framework and its formulation/computation holds in any dimension; in other words, it is valid in [Formula: see text], n ≥ 1 . We show how this mixed gradient can be used to construct image priors that take edge orientation, as well as intensity, into account, and then involved in various image processing tasks while preserving curvilinear structures. The experiments carried out on 2D, 3D, real, and synthetic images illustrate the relevance of the proposed gradient, and its use in variational frameworks for both denoising and segmentation tasks.

Detection of lobular structures in normal breast tissue.

BACKGROUND: Ongoing research into inflammatory conditions raises an increasing need to evaluate immune cells in histological sections in biologically relevant regions of interest (ROIs). Herein, we compare different approaches to automatically detect lobular structures in human normal breast tissue in digitized whole slide images (WSIs). This automation is required to perform objective and consistent quantitative studies on large data sets. METHODS: In normal breast tissue from nine healthy patients immunohistochemically stained for different markers, we evaluated and compared three different image analysis methods to automatically detect lobular structures in WSIs: (1) a bottom-up approach using the cell-based data for subsequent tissue level classification, (2) a top-down method starting with texture classification at tissue level analysis of cell densities in specific ROIs, and (3) a direct texture classification using deep learning technology. RESULTS: All three methods result in comparable overall quality allowing automated detection of lobular structures with minor advantage in sensitivity (approach 3), specificity (approach 2), or processing time (approach 1). Combining the outputs of the approaches further improved the precision. CONCLUSIONS: Different approaches of automated ROI detection are feasible and should be selected according to the individual needs of biomarker research. Additionally, detected ROIs could be used as a basis for quantification of immune infiltration in lobular structures.

Connected filtering based on multivalued component-trees.

In recent papers, a new notion of component-graph was introduced. It extends the classical notion of component-tree initially proposed in mathematical morphology to model the structure of gray-level images. Component-graphs can indeed model the structure of any-gray-level or multivalued-images. We now extend the antiextensive filtering scheme based on component-trees, to make it tractable in the framework of component-graphs. More precisely, we provide solutions for building a component-graph, reducing it based on selection criteria, and reconstructing a filtered image from a reduced component-graph. In this paper, we first consider the cases where component-graphs still have a tree structure; they are then called multivalued component-trees. The relevance and usefulness of such multivalued component-trees are illustrated by applicative examples on hierarchically classified remote sensing images.

SNR enhancement of highly-accelerated real-time cardiac MRI acquisitions based on non-local means algorithm.

Real-time cardiac MRI appears as a promising technique to evaluate the mechanical function of the heart. However, ultra-fast MRI acquisitions come with an important signal-to-noise ratio (SNR) penalty, which drastically reduces the image quality. Hence, a real-time denoising approach would be desirable for SNR amelioration. In the clinical context of cardiac dysfunction assessment, long acquisitions are required and for most patients the acquisition takes place with free breathing. Hence, it is necessary to compensate respiratory motion in real-time. In this article, a real-time and interactive method for sequential registration and denoising of real-time MR cardiac images is presented. The method has been experimented on 60 fast MRI acquisitions in five healthy volunteers and five patients. These experiments assessed the feasibility of the method in a real-time context.

Using mathematical morphology for the anatomical labeling of vertebrae from 3D CT-scan images.

In this article we propose an original method for the anatomical labeling of vertebrae from 3D CT-scan images. The primary purpose of this work is to obtain a robust referential of the abdomen. This referential can be used to locate anatomical structures like organs or blood vessels. The main problematic concerns the separation of the vertebrae, which are structures that are very close from each other. In order to detect the intervertebral spaces, we use a morphological operator which detects the dark spaces corresponding to intervertebral discs in combination with an analysis of the shape of the vertebrae in the axial plane. To reconstruct the vertebrae we use the paradigm of mathematical morphology, which consists in finding markers inside the vertebrae and compute the watershed from markers. Then we label the vertebrae according to their anatomical names. To do this, we automatically detect T12 vertebrae. We have evaluated our algorithm on 26 images.