Chemical characterization of atmospheric particulate matter in Friuli Venezia Giulia (NE Italy) by exploratory data analysis with multisite and multivariate approach

Abstract

The chemical composition of atmospheric particulate (PM10) in the Friuli Venezia Giulia (FVG) region (NE Italy) has been characterized for the first time with the help of exploratory data analysis (EDA) techniques (uni-, bi-, and multivariated, i.e., principal components analysis), molecular and elemental diagnostic ratios, and seasonal trends. Despite that the available analytical data was limited to the parameters routinely analyzed on PM10 by ARPA FVG (11 elements and 16 PAH congeners), the large number of samples and of measured chemical parameters, together with the applied techniques of data analysis, allowed us to extract useful latent information from the dataset, resulting in a greater knowledge of both regional and local features. Specifically, we succeeded in matching data patterns to the known pollution sources of some sampling stations, both industrial (two secondary fusion steelworks and one coke oven) and urban (traffic and domestic heating), and in defining the mainly urban or mainly industrial feature of some questionable sampling stations. This is of paramount importance to check for possible industrial inputs in urban stations, allowing policymakers to implement the most appropriate response.

A deep learning framework for segmentation and pose estimation of pedicle screw implants based on C-arm fluoroscopy

Abstract

Purpose

Pedicle screw fixation is a challenging procedure with a concerning rates of reoperation. After insertion of the screws is completed, the most common intraoperative verification approach is to acquire anterior–posterior and lateral radiographic images, based on which the surgeons try to visually assess the correctness of insertion. Given the limited accuracy of the existing verification techniques, we identified the need for an accurate and automated pedicle screw assessment system that can verify the screw insertion intraoperatively. For doing so, this paper offers a framework for automatic segmentation and pose estimation of pedicle screws based on deep learning principles.

Methods

Segmentation of pedicle screw X-ray projections was performed by a convolutional neural network. The network could isolate the input X-rays into three classes: screw head, screw shaft and background. Once all the screw shafts were segmented, knowledge about the spatial configuration of the acquired biplanar X-rays was used to identify the correspondence between the projections. Pose estimation was then performed to estimate the 6 degree-of-freedom pose of each screw. The performance of the proposed pose estimation method was tested on a porcine specimen.

Results

The developed machine learning framework was capable of segmenting the screw shafts with 93% and 83% accuracy when tested on synthetic X-rays and on clinically realistic X-rays, respectively. The pose estimation accuracy of this method was shown to be \(1.93^{\circ } \pm 0.64^{\circ }\) and \(1.92 \pm 0.55\,\hbox {mm}\) on clinically realistic X-rays.

Conclusions

The proposed system offers an accurate and fully automatic pedicle screw segmentation and pose assessment framework. Such a system can help to provide an intraoperative pedicle screw insertion assessment protocol with minimal interference with the existing surgical routines.

Oropharyngeal squamous cell carcinoma induces an innate systemic inflammation, affected by the size of the tumour and the lymph node spread

Clinical Otolaryngology, EarlyView.

Resonance frequency analysis in bone‐anchored hearing aids: Patient demographics and an approach to assess implant stability

Clinical Otolaryngology, EarlyView.

Physical outcome measures for conductive and mixed hearing loss treatment: A systematic review

Clinical Otolaryngology, EarlyView.

Gastrointestinal stromal tumours: ESMO–EURACAN Clinical Practice Guidelines for diagnosis, treatment and follow-up†

Soft tissue and visceral sarcomas: ESMO–EURACAN Clinical Practice Guidelines for diagnosis, treatment and follow-up†

Soft tissue sarcomas (STSs) gather over 80 histological entities, with even more molecular subsets, characterised by a low to very low incidence in all populations. The majority of sarcomas arise from the soft tissue (close to 75%), with ∼15% gastrointestinal stromal tumours (GISTs) and 10% bone sarcomas. These ESMO–EURACAN (European Society for Medical Oncology–European Reference Network for rare adult solid cancers) Clinical Practice Guidelines cover STSs, while GISTs are covered by dedicated ESMO–EURACAN Clinical Practice Guidelines [1]. Kaposi's sarcoma is not considered in the present document. Extraskeletal Ewing and Ewing-like sarcoma is covered by ESMO Clinical Practice Guidelines on bone sarcomas [2]. In general, the same principles for these tumours in children apply to adults. This is also the case for embryonal and alveolar rhabdomyosarcomas, which are exceedingly rare in adults. On the other hand, pleomorphic rhabdomyosarcoma is viewed as a high-grade, adult-type STS. Extraskeletal osteosarcoma is also a high-grade STS, whose clinical resemblance with osteosarcoma of bone is doubtful (prospective collection of data is encouraged to generate evidence on the therapeutic implications of such a diagnosis). Adult STS pathological subtypes occurring in adolescents should be managed the same way as in adult patients, though the same histotype might display clinical peculiarities when occurring at different ages.

A case of food-dependent exercise-induced angioedema

Publication date: Available online 26 May 2018
Source:The Journal of Allergy and Clinical Immunology: In Practice
Author(s): Eli Magen, Tinatin Chikovani

Eritema difuso e hiperqueratosis acral en un lactante

Publication date: Available online 26 May 2018
Source:Actas Dermo-Sifiliográficas
Author(s): A. Gómez-Zubiaur, I. Spanoudi-Kitrimi, A. Torrelo

Extreme weather event may induce Microcystis blooms in the Qiantang River, Southeast China

Abstract

A severe cyanobacterial bloom in the mainstem of a large Chinese river was first reported from China. The Qiantang River is the longest river in the Zhejiang province, southeast China. It provides drinking water supply to ~ 16 million people, including Hangzhou city. Fifteen sites along the Qiantang River (including upper, middle (Fuchunjiang Reservoir), and lower reaches and tributaries) were sampled between August 13 and September 9, 2016 to conduct a preliminary examination of the outbreak of Microcystis blooms. Laboratory investigation revealed that Microcystis spp. are dominant in the Fuchunjiang Reservoir (an overflow reservoir on the mainstem of the Qiantang River) with an extremely high cell density of 2.3 × 10⁸ cells/L, leading to a severe bloom in the mainstem of the Qiantang River. Investigations of the meteorological, hydrological, and nutrient characteristics associated with the bloom indicated that extremely dry (6.8 mm rainfall from August 13 to September 9, 2016) and hot (32 consecutive days of temperatures > 30 °C from July 20 to August 31, 2016) weather might be the key factors triggering the bloom. Additionally, the extremely low flow of the tributary, Lanjiang River (142 ± 56 m³/s from August 13 to September 9), and its high nutrient background, favored the bloom. While nutrient reductions are important, the most immediate and effective management approach might be to implement appropriate minimal flow conditions to mitigate the blooms.

Fast and automatic bone segmentation and registration of 3D ultrasound to CT for the full pelvic anatomy: a comparative study

Abstract

Purpose

Ultrasound (US) is a safer alternative to X-rays for bone imaging, and its popularity for orthopedic surgical navigation is growing. Routine use of intraoperative US for navigation requires fast, accurate and automatic alignment of tracked US to preoperative computed tomography (CT) patient models. Our group previously investigated image segmentation and registration to align untracked US to CT of only the partial pelvic anatomy. In this paper, we extend this to study the performance of these previously published techniques over the full pelvis in a tracked framework, to characterize their suitability in more realistic scenarios, along with an additional simplified segmentation method and similarity metric for registration.

Method

We evaluated phase symmetry segmentation, and Gaussian mixture model (GMM) and coherent point drift (CPD) registration methods on a pelvic phantom augmented with human soft tissue images. Additionally, we proposed and evaluated a simplified 3D bone segmentation algorithm we call Shadow–Peak (SP), which uses acoustic shadowing and peak intensities to detect bone surfaces. We paired this with a registration pipeline that optimizes the normalized cross-correlation (NCC) between distance maps of the segmented US–CT images.

Results

SP segmentation combined with the proposed NCC registration successfully aligned tracked US volumes to the preoperative CT model in all trials, in contrast to the other techniques. SP with NCC achieved a median target registration error (TRE) of 2.44 mm (maximum 4.06 mm), when imaging all three anterior pelvic structures, and a mean runtime of 27.3 s. SP segmentation with CPD registration was the next most accurate combination: median TRE of 3.19 mm (maximum 6.07 mm), though a much faster runtime of 4.2 s.

Conclusion

We demonstrate an accurate, automatic image processing pipeline for intraoperative alignment of US–CT over the full pelvis and compare its performance with the state-of-the-art methods. The proposed methods are amenable to clinical implementation due to their high accuracy on realistic data and acceptably low runtimes.

The intelligent OR: design and validation of a context-aware surgical working environment

Abstract

Purpose

Interoperability of medical devices based on standards starts to establish in the operating room (OR). Devices share their data and control functionalities. Yet, the OR technology rarely implements cooperative, intelligent behavior, especially in terms of active cooperation with the OR team. Technical context-awareness will be an essential feature of the next generation of medical devices to address the increasing demands to clinicians in information seeking, decision making, and human–machine interaction in complex surgical working environments.

Methods

The paper describes the technical validation of an intelligent surgical working environment for endoscopic ear–nose–throat surgery. We briefly summarize the design of our framework for context-aware system's behavior in integrated OR and present example realizations of novel assistance functionalities. In a study on patient phantoms, twenty-four procedures were implemented in the proposed intelligent surgical working environment based on recordings of real interventions. Subsequently, the whole processing pipeline for context-awareness from workflow recognition to the final system's behavior is analyzed.

Results

Rule-based behavior that considers multiple perspectives on the procedure can partially compensate recognition errors. A considerable robustness could be achieved with a reasonable quality of the recognition. Overall, reliable reactive as well as proactive behavior of the surgical working environment can be implemented in the proposed environment.

Conclusions

The obtained validation results indicate the suitability of the overall approach. The setup is a reliable starting point for a subsequent evaluation of the proposed context-aware assistance. The major challenge for future work will be to implement the complex approach in a cross-vendor setting.

A virtual pointer to support the adoption of professional vision in laparoscopic training

Abstract

Purpose

To assess a virtual pointer in supporting surgical trainees' development of professional vision in laparoscopic surgery.

Methods

We developed a virtual pointing and telestration system utilizing the Microsoft Kinect movement sensor as an overlay for any imagine system. Training with the application was compared to a standard condition, i.e., verbal instruction with un-mediated gestures, in a laparoscopic training environment. Seven trainees performed four simulated laparoscopic tasks guided by an experienced surgeon as the trainer. Trainee performance was subjectively assessed by the trainee and trainer, and objectively measured by number of errors, time to task completion, and economy of movement.

Results

No significant differences in errors and time to task completion were obtained between virtual pointer and standard conditions. Economy of movement in the non-dominant hand was significantly improved when using virtual pointer ( \(p = 0.012\) ). The trainers perceived a significant improvement in trainee performance in virtual pointer condition ( \(p < 0.001\) ), while the trainees perceived no difference. The trainers' perception of economy of movement was similar between the two conditions in the initial three runs and became significantly improved in virtual pointer condition in the fourth run ( \(p = 0.017\) ).

Conclusions

Results show that the virtual pointer system improves the trainer's perception of trainee's performance and this is reflected in the objective performance measures in the third and fourth training runs. The benefit of a virtual pointing and telestration system may be perceived by the trainers early on in training, but this is not evident in objective trainee performance until further mastery has been attained. In addition, the performance improvement of economy of motion specifically shows that the virtual pointer improves the adoption of professional vision— improved ability to see and use laparoscopic video results in more direct instrument movement.

Computer-assisted liver graft steatosis assessment via learning-based texture analysis

Abstract

Purpose

Fast and accurate graft hepatic steatosis (HS) assessment is of primary importance for lowering liver dysfunction risks after transplantation. Histopathological analysis of biopsied liver is the gold standard for assessing HS, despite being invasive and time consuming. Due to the short time availability between liver procurement and transplantation, surgeons perform HS assessment through clinical evaluation (medical history, blood tests) and liver texture visual analysis. Despite visual analysis being recognized as challenging in the clinical literature, few efforts have been invested to develop computer-assisted solutions for HS assessment. The objective of this paper is to investigate the automatic analysis of liver texture with machine learning algorithms to automate the HS assessment process and offer support for the surgeon decision process.

Methods

Forty RGB images of forty different donors were analyzed. The images were captured with an RGB smartphone camera in the operating room (OR). Twenty images refer to livers that were accepted and 20 to discarded livers. Fifteen randomly selected liver patches were extracted from each image. Patch size was \(100\times 100\) . This way, a balanced dataset of 600 patches was obtained. Intensity-based features (INT), histogram of local binary pattern ( \(H__{riu2}}\) ), and gray-level co-occurrence matrix ( \(F_{\mathrm{GLCM}}\) ) were investigated. Blood-sample features (Blo) were included in the analysis, too. Supervised and semisupervised learning approaches were investigated for feature classification. The leave-one-patient-out cross-validation was performed to estimate the classification performance.

Results

With the best-performing feature set ( \(H__{riu2}}+\hbox {INT}+\hbox {Blo}\) ) and semisupervised learning, the achieved classification sensitivity, specificity, and accuracy were 95, 81, and 88%, respectively.

Conclusions

This research represents the first attempt to use machine learning and automatic texture analysis of RGB images from ubiquitous smartphone cameras for the task of graft HS assessment. The results suggest that is a promising strategy to develop a fully automatic solution to assist surgeons in HS assessment inside the OR.

A PRM approach for early prediction of breast cancer response to chemotherapy based on registered MR images

Abstract

Purpose

This study aims to provide and optimize a performing algorithm for predicting the breast cancer response rate to the first round of chemotherapy using Magnetic Resonance Imaging (MRI). This provides an early recognition of breast tumor reaction to chemotherapy by using the Parametric Response Map (PRM) method.

Methods

PRM may predict the breast cancer response to chemotherapy by analyzing voxel-by-voxel temporal intra-tumor changes during one round of chemotherapy. Indeed, the tumor recognizes intra-tumor changes concerning its vascularity, which is an important criterion in the present study. This method is mainly based on spatial image affine registration between the breast tumor MRI volumes, acquired before and after the first cycle of chemotherapy, and region growing segmentation of the tumor volume. To evaluate our method, we used a retrospective study of 40 patients provided by a collaborating institute.

Results

PRM allows a color map to be created with the percentages of positive, negative and stable breast tumor response during the first round of chemotherapy, identifying each region with its response rate. We assessed the accuracy of the proposed method using technical and medical validation methods. The technical validation was based on landmarks-based registration and fully manual segmentation. The medical evaluation was based on the accuracy calculation of the standard reference of anatomic pathology. The p-values and the Area Under the Curve (AUC) of the Receiver Operating Characteristics were calculated to evaluate the proposed PRM method.

Conclusion

We performed and evaluated the proposed PRM method to study and analyze the behavior of a tumor during the first round of chemotherapy, based on the intra-tumor changes of MR breast tumor images. The AUC obtained for the PRM method is considered as relevant in the early prediction of breast tumor response.

From ideas to long-term studies: 3D printing clinical trials review

Abstract

Purpose

Although high costs are often cited as the main limitation of 3D printing (3DP) in the medical field, current lack of clinical evidence is asserting itself as an impost as the field begins to mature. The aim is to review clinical trials in the field of 3DP, an area of research which has grown dramatically in recent years.

Methods

We surveyed clinical trials registered in 15 primary registries worldwide, including ClinicalTrials.gov. All trials which utilized 3DP in a clinical setting were included in this review. Our search was performed on December 15, 2017. Data regarding the purpose of the study, inclusion criteria, number of patients enrolled, primary outcomes, centers, start and estimated completion dates were extracted.

Results

A total of 92 clinical trials with \({N}=6\) 252 patients matched the criteria and were included in the study. A total of 42 (45.65%) studies cited China as their location. Only 10 trials were multicenter and 2 were registered as international. The discipline that most commonly utilized 3DP was Orthopedic Surgery, with 25 (27.17%) registered trials. At the time of data extraction, 17 (18.48%) clinical trials were complete.

Conclusions

After several years of case reports, feasibility studies and technical reports in the field, larger-scale studies are beginning to emerge. There are almost no international register entries. Although there are new emerging areas of study in disciplines that may benefit from 3DP, it is likely to remain limited to very specific applications.

A novel technology for 3D knee prosthesis planning and treatment evaluation using 2D X-ray radiographs: a clinical evaluation

Abstract

Purpose

 To present a clinical validation of a novel technology called "3X" which allows for 3D prosthesis planning and treatment evaluation in total knee arthroplasty (TKA) using only 2D X-ray radiographs.

Materials and methods

 After local institution review board approvals, 3X was evaluated on 43 cases (23 for preoperative planning and 20 for postoperative treatment evaluation). All the patients underwent CT scans according to a standard protocol. The results measured on the CT data were regarded as the ground truth. Additionally, two X-ray images were acquired for each affected leg and were used by 3X technology to derive patient-specific measurements of the leg. In total, we compared seven parameters for planning TKA and five parameters for postoperative prosthesis alignment.

Results

 Our experimental results demonstrated that the mean distances between the surface models reconstructed from 2D X-rays and the associated surface models obtained from 3D CT data were smaller than 1.5 mm. The average differences for all angular parameters were smaller than \(1.5^{\circ }\) . In over 78% cases 3X technology derived the same femoral component size as the CT-based ground truth and this value went down to 70% when 3X technology was used to predict the size of tibial component.

Conclusion

 3X is a technology that allows for true 3D preoperative planning and postoperative treatment evaluation based on 2D X-ray radiographs.

Intraoperative stent segmentation in X-ray fluoroscopy for endovascular aortic repair

Abstract

Purpose

Fusion of preoperative data with intraoperative X-ray images has proven the potential to reduce radiation exposure and contrast agent, especially for complex endovascular aortic repair (EVAR). Due to patient movement and introduced devices that deform the vasculature, the fusion can become inaccurate. This is usually detected by comparing the preoperative information with the contrasted vessel. To avoid repeated use of iodine, comparison with an implanted stent can be used to adjust the fusion. However, detecting the stent automatically without the use of contrast is challenging as only thin stent wires are visible.

Method

We propose a fast, learning-based method to segment aortic stents in single uncontrasted X-ray images. To this end, we employ a fully convolutional network with residual units. Additionally, we investigate whether incorporation of prior knowledge improves the segmentation.

Results

We use 36 X-ray images acquired during EVAR for training and evaluate the segmentation on 27 additional images. We achieve a Dice coefficient of 0.933 (AUC 0.996) when using X-ray alone, and 0.918 (AUC 0.993) and 0.888 (AUC 0.99) when adding the preoperative model, and information about the expected wire width, respectively.

Conclusion

The proposed method is fully automatic, fast and segments aortic stent grafts in fluoroscopic images with high accuracy. The quality and performance of the segmentation will allow for an intraoperative comparison with the preoperative information to assess the accuracy of the fusion.

CARS 2018—Computer Assisted Radiology and Surgery Proceedings of the 32nd International Congress and Exhibition Berlin, Germany, June 20–23, 2018

Prostate segmentation in MRI using a convolutional neural network architecture and training strategy based on statistical shape models

Abstract

Purpose

Most of the existing convolutional neural network (CNN)-based medical image segmentation methods are based on methods that have originally been developed for segmentation of natural images. Therefore, they largely ignore the differences between the two domains, such as the smaller degree of variability in the shape and appearance of the target volume and the smaller amounts of training data in medical applications. We propose a CNN-based method for prostate segmentation in MRI that employs statistical shape models to address these issues.

Methods

Our CNN predicts the location of the prostate center and the parameters of the shape model, which determine the position of prostate surface keypoints. To train such a large model for segmentation of 3D images using small data (1) we adopt a stage-wise training strategy by first training the network to predict the prostate center and subsequently adding modules for predicting the parameters of the shape model and prostate rotation, (2) we propose a data augmentation method whereby the training images and their prostate surface keypoints are deformed according to the displacements computed based on the shape model, and (3) we employ various regularization techniques.

Results

Our proposed method achieves a Dice score of 0.88, which is obtained by using both elastic-net and spectral dropout for regularization. Compared with a standard CNN-based method, our method shows significantly better segmentation performance on the prostate base and apex. Our experiments also show that data augmentation using the shape model significantly improves the segmentation results.

Conclusions

Prior knowledge about the shape of the target organ can improve the performance of CNN-based segmentation methods, especially where image features are not sufficient for a precise segmentation. Statistical shape models can also be employed to synthesize additional training data that can ease the training of large CNNs.