New findings of Prototherium ausetanum (Mammalia, Pan-Sirenia) from paving stones in Girona (Catalonia, Spain)?

Taxonomic and morphological approaches on Eocene sirenians from Catalonia (Spain) benefit from a newly discovered specimen found in a quite unusual locality, the pedestrian zone in the city of Girona. Two fossil-bearing limestone slabs from middle Eocene (Bartonian) layers of a quarry in the wider surrounding area north-west of Barcelona, were CT-scanned in the Clínica Girona to enhance more detailed investigations. Post-processing of the scans and, as far as possible, 3D-reconstruction of the preserved elements in the slabs was performed at Museum für Naturkunde Berlin. Thereby, a skull of a Dugong specimen was used as a reference point. Based on the combined analysis of macroscopic and CT-data, the specimen most likely represents Prototherium ausetanum Balaguer & Alba, 2016 and complements the available information of the holotype and hitherto only known specimen of that species. The Girona specimen is an adult, but small individual that corroborates P. ausetanum as a generally small-sized species compared to other known Prototherium taxa

One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks

In recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple sclerosis (MS) patient images, due to their superior performance compared with those of other state-of-the-art methods. However, the accuracies of CNN methods tend to decrease significantly when evaluated on different image domains compared with those used for training, which demonstrates the lack of adaptability of CNNs to unseen imaging data. In this study, we analyzed the effect of intensity domain adaptation on our recently proposed CNN-based MS lesion segmentation method. Given a source model trained on two public MS datasets, we investigated the transferability of the CNN model when applied to other MRI scanners and protocols, evaluating the minimum number of annotated images needed from the new domain and the minimum number of layers needed to re-train to obtain comparable accuracy. Our analysis comprised MS patient data from both a clinical center and the public ISBI2015 challenge database, which permitted us to compare the domain adaptation capability of our model to that of other state-of-the-art methods. For the ISBI2015 challenge, our one-shot domain adaptation model trained using only a single image showed a performance similar to that of other CNN methods that were fully trained using the entire available training set, yielding a comparable human expert rater performance. We believe that our experiments will encourage the MS community to incorporate its use in different clinical settings with reduced amounts of annotated data. This approach could be meaningful not only in terms of the accuracy in delineating MS lesions but also in the related reductions in time and economic costs derived from manual lesion labeling

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

International audienceProstate cancer is the second most diagnosed cancer of men all over the world. In the last decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed improving diagnosis.In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systemshave been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field ofresearch for the last ten years. This survey aims to provide a comprehensive review of the state of the art in this lapse of time, focusing on the different stages composing the work-flow of a computer-aidedsystem. We also provide a comparison between studies and a discussion about the potential avenues for future research. In addition, this paper presents a new public online dataset which is made available to theresearch community with the aim of providing a common evaluation framework to overcome some of the current limitations identified in this survey

One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks

Automatic lesion segmentation; Convolutional neural networks; Multiple sclerosisSegmentació automàtica de les lesions ; Xarxes neuronals convolucionals; Esclerosi múltipleSegmentación automática de las lesiones ; Redes neuronales convolucionales; Esclerosis múltipleIn recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple sclerosis (MS) patient images, due to their superior performance compared with those of other state-of-the-art methods. However, the accuracies of CNN methods tend to decrease significantly when evaluated on different image domains compared with those used for training, which demonstrates the lack of adaptability of CNNs to unseen imaging data. In this study, we analyzed the effect of intensity domain adaptation on our recently proposed CNN-based MS lesion segmentation method. Given a source model trained on two public MS datasets, we investigated the transferability of the CNN model when applied to other MRI scanners and protocols, evaluating the minimum number of annotated images needed from the new domain and the minimum number of layers needed to re-train to obtain comparable accuracy. Our analysis comprised MS patient data from both a clinical center and the public ISBI2015 challenge database, which permitted us to compare the domain adaptation capability of our model to that of other state-of-the-art methods. In both datasets, our results showed the effectiveness of the proposed model in adapting previously acquired knowledge to new image domains, even when a reduced number of training samples was available in the target dataset. For the ISBI2015 challenge, our one-shot domain adaptation model trained using only a single case showed a performance similar to that of other CNN methods that were fully trained using the entire available training set, yielding a comparable human expert rater performance. We believe that our experiments will encourage the MS community to incorporate its use in different clinical settings with reduced amounts of annotated data. This approach could be meaningful not only in terms of the accuracy in delineating MS lesions but also in the related reductions in time and economic costs derived from manual lesion labeling

Proton magnetic resonance spectroscopy in oncology: the fingerprints of cancer?

Abnormal metabolism is a key tumor hallmark. Proton magnetic resonance spectroscopy (1H-MRS) allows measurement of metabolite concentration that can be utilized to characterize tumor metabolic changes. 1H-MRS measurements of specific metabolites have been implemented in the clinic. This article performs a systematic review of image acquisition and interpretation of 1H-MRS for cancer evaluation, evaluates its strengths and limitations, and correlates metabolite peaks at 1H-MRS with diagnostic and prognostic parameters of cancer in different tumor types

How clinical imaging can assess cancer biology.

Human cancers represent complex structures, which display substantial inter- and intratumor heterogeneity in their genetic expression and phenotypic features. However, cancers usually exhibit characteristic structural, physiologic, and molecular features and display specific biological capabilities named hallmarks. Many of these tumor traits are imageable through different imaging techniques. Imaging is able to spatially map key cancer features and tumor heterogeneity improving tumor diagnosis, characterization, and management. This paper aims to summarize the current and emerging applications of imaging in tumor biology assessment

Prediction of neonatal respiratory morbidity by quantitative ultrasound lung texture analysis: a multicenter study.

BACKGROUND: Prediction of neonatal respiratory morbidity may be useful to plan delivery in complicated pregnancies. The limited predictive performance of the current diagnostic tests together with the risks of an invasive procedure restricts the use of fetal lung maturity assessment. OBJECTIVE: The objective of the study was to evaluate the performance of quantitative ultrasound texture analysis of the fetal lung (quantusFLM) to predict neonatal respiratory morbidity in preterm and early-term (<39.0 weeks) deliveries. STUDY DESIGN: This was a prospective multicenter study conducted in 20 centers worldwide. Fetal lung ultrasound images were obtained at 25.0-38.6 weeks of gestation within 48 hours of delivery, stored in Digital Imaging and Communication in Medicine format, and analyzed with quantusFLM. Physicians were blinded to the analysis. At delivery, perinatal outcomes and the occurrence of neonatal respiratory morbidity, defined as either respiratory distress syndrome or transient tachypnea of the newborn, were registered. The performance of the ultrasound texture analysis test to predict neonatal respiratory morbidity was evaluated. RESULTS: A total of 883 images were collected, but 17.3% were discarded because of poor image quality or exclusion criteria, leaving 730 observations for the final analysis. The prevalence of neonatal respiratory morbidity was 13.8% (101 of 730). The quantusFLM predicted neonatal respiratory morbidity with a sensitivity, specificity, positive and negative predictive values of 74.3% (75 of 101), 88.6% (557 of 629), 51.0% (75 of 147), and 95.5% (557 of 583), respectively. Accuracy was 86.5% (632 of 730) and positive and negative likelihood ratios were 6.5 and 0.3, respectively. CONCLUSION: The quantusFLM predicted neonatal respiratory morbidity with an accuracy similar to that previously reported for other tests with the advantage of being a noninvasive technique

A boosting approach for prostate cancer detection using multi-parametric MRI

International audienceProstate cancer has been reported as the second most frequently diagnosed men cancers in the world. In the last decades, new imaging techniques based on MRI have been developed in order to improve the diagnosis task of radiologists. In practise, diagnosis can be affected by multiple factors reducing the chance to detect potential lesions. Computer-aided detection and computer-aided diagnosis have been designed to answer to these needs and provide help to radiologists in their daily duties. In this study, we proposed an automatic method to detect prostate cancer from a per voxel manner using 3T multi-parametric Magnetic Resonance Imaging (MRI) and a gradient boosting classifier. The best performances are obtained using all multi-parametric information as well as zonal information. The sensitivity and specificity obtained are 94.7% and 93.0%, respectively and an Area Under Curve (AUC) of 0.968

Imaging muscoloscheletrico100 casi dalla pratica clinica /

XVI, 224 pagg.online resource