Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods

BENCHMARKING DEEP LEARNING FRAMEWORKS FOR THE CLASSIFICATION OF VERY HIGH RESOLUTION SATELLITE MULTISPECTRAL DATA

In this paper we evaluated deep-learning frameworks based on Convolutional Neural Networks for the accurate classification of multispectral remote sensing data. Certain state-of-the-art models have been tested on the publicly available SAT-4 and SAT-6 high resolution satellite multispectral datasets. In particular, the performed benchmark included the AlexNet, AlexNet-small and VGG models which had been trained and applied to both datasets exploiting all the available spectral information. Deep Belief Networks, Autoencoders and other semi-supervised frameworks have been, also, compared. The high level features that were calculated from the tested models managed to classify the different land cover classes with significantly high accuracy rates i.e., above 99.9%. The experimental results demonstrate the great potentials of advanced deep-learning frameworks for the supervised classification of high resolution multispectral remote sensing data

The role of sclerostin/dickkopf-1 and receptor activator of nuclear factor kB ligand/osteoprotegerin signalling pathways in the development of osteoporosis in patients with haemophilia A and B: A cross-sectional study

Aim: Haemophilia A and B are associated with reduced bone mineral density (BMD). The aim of this study was to assess circulating sclerostin and dickkopf-1 (Dkk-1), (inhibitors of osteoblastic differentiation), as well as the receptor activator of nuclear factor kB ligand (RANKL)/osteoprotegerin (OPG) system (the major regulator of osteoclastogenesis), in patients with haemophilia (PWH), their possible correlations with clinical risk factors and the effect of ibandronate on these markers. Methods: Eighty-nine male PWH (mean age 45.9 ± 15.3 years) and 30 age-matched healthy male controls participated. BMD was assessed by DXA. Sclerostin, Dkk-1, RANKL and OPG were measured in serum of patients, controls, as well as in ten patients receiving oral ibandronate (150 mg/mo), at baseline and after 12 months. Results: Patients with haemophilia had lower circulating sclerostin (median ± IQR: 47.4 ± 26.93 vs 250 ± 250 pmol/L, P <.001), Dkk-1 (21.24 ± 17.18 vs 26.16 ± 15.32pg/mL, P =.04) and higher levels of RANKL (0.23 ± 0.03 vs 0.04 ± 0.03 pmol/L, P =.001), RANKL/OPG ratio (0.063 ± 0.25 vs 0.005 ± 0.11, P =.001) compared with controls. Patients with low BMD had higher OPG concentrations compared to those with normal BMD. Sclerostin and RANKL/OPG correlated positively with BMD. Patients with severe haemophilia had lower sclerostin concentrations compared with those with mild or moderate disease. The degree of arthropathy negatively correlated with sclerostin and Dkk-1 levels. PWH who received ibandronate showed a decrease in serum Dkk-1 without any significant effect on sclerostin and RANKL/OPG. Conclusions: Patients with haemophilia present increased osteoclastic activity coupled with compensatory increased osteoblastic activity. Ibandronate did not affect RANKL/OPG ratio, but it decreased Dkk-1. © 2017 John Wiley & Sons Lt

Acute Leukemia and Pregnancy

The combination of acute leukemia and pregnancy is infrequent. It is estimated to occur in less than 1 in 75,000 pregnancies. Maternal and fetal outcomes have improved substantially in recent years. In general, multi-agent chemotherapy is given as soon as the diagnosis of leukemia is established, even if it is in the first trimester. There are two important considerations in the management of a patient with leukemia during pregnancy, the mother who needs optimal cancer therapy and the developing fetus who could potentially be affected by the disease and/or the teratogenicity of antineoplactic agents. Vaginal delivery is preferable, and caesarian section is reserved for obstetrical indications only

Context Aware 3D CNNs for Brain Tumor Segmentation

International audienceIn this work we propose a novel deep learning based pipeline for the task of brain tumor segmentation. Our pipeline consists of three primary components: (i) a preprocessing stage that exploits histogram standardization to mitigate inaccuracies in measured brain modalities, (ii) a first prediction stage that uses the V-Net deep learning architecture to output dense, per voxel class probabilities, and (iii) a prediction refinement stage that uses a Conditional Random Field (CRF) with a bilateral filtering objective for better context awareness. Additionally, we compare the V-Net architecture with a custom 3D Residual Network architecture, trained on a multi-view strategy, and our ablation experiments indicate that V-Net outperforms the 3D ResNet-18 with all bells and whistles, while fully connected CRFs as post processing, boost the performance of both networks. We report competitive results on the BraTS 2018 validation and test set