Deep learning-based fully automatic segmentation of wrist cartilage in MR images

The study objective was to investigate the performance of a dedicated convolutional neural network (CNN) optimized for wrist cartilage segmentation from 2D MR images. CNN utilized a planar architecture and patch-based (PB) training approach that ensured optimal performance in the presence of a limited amount of training data. The CNN was trained and validated in twenty multi-slice MRI datasets acquired with two different coils in eleven subjects (healthy volunteers and patients). The validation included a comparison with the alternative state-of-the-art CNN methods for the segmentation of joints from MR images and the ground-truth manual segmentation. When trained on the limited training data, the CNN outperformed significantly image-based and patch-based U-Net networks. Our PB-CNN also demonstrated a good agreement with manual segmentation (Sorensen-Dice similarity coefficient (DSC) = 0.81) in the representative (central coronal) slices with large amount of cartilage tissue. Reduced performance of the network for slices with a very limited amount of cartilage tissue suggests the need for fully 3D convolutional networks to provide uniform performance across the joint. The study also assessed inter- and intra-observer variability of the manual wrist cartilage segmentation (DSC=0.78-0.88 and 0.9, respectively). The proposed deep-learning-based segmentation of the wrist cartilage from MRI could facilitate research of novel imaging markers of wrist osteoarthritis to characterize its progression and response to therapy

Noncovalent interactions in crowded olefinic radical cations

Peer reviewe

Centrality evolution of the charged-particle pseudorapidity density over a broad pseudorapidity range in Pb-Pb collisions at root s(NN)=2.76TeV

Peer reviewe

Magnetic resonance imaging prospects of prognostic value in women pelvic pathology

Aim of the study was to estimate the prognostic value of perfusion and diffusion magnetic resonance imaging (MRI) methods in systemic evaluation of perifocal changes of malignant masses in women’s pelvis. Material and methods. A retrospective analysis of MRI of the pelvic organs in women was performed on MRI scanners with a magnetic field of 1,5 T (Philips Achieva, Netherlands). The retrospective study included 1,5 T MRI of 530 women with pelvis pathology. It consists of 50 % (265 cases) of malignant pelvic mass and 50 % (265 cases) of non tumor pathologies. The most common malignant pathologies were regarded: rectum cancer (n = 61), ovarian cancer (n = 62), uterine cancer (n = 65), cervical cancer (n = 77). Group with non-tumor pathology was taken as comparison group and included inflammation (abscess), adhesions and other non-tumor pelvic pathologies (pseudocyst of peritoneum, hemorrhagic cyst, endometriosis). In all cases a body coil was used on pelvic region. MRI protocols included T2-, T1 - weighted imaging, diffusion weighted imaging, postcontrast T1 - weighted imaging. apparent diffusion coefficient value and perfusion value were estimated. Results and discussion. In intergroup comparison with systemic evaluation of MRI with ANOVA we revealed that the diffusion restriction and the apparent diffusion coefficient bear potential value for disease prognosis. Conclusions. MRI can be of value not only for evaluation of local spread, as well as providing wide opportunities for differential diagnosis and use of MRI as biomarker in some diseases. Such indications as restricted diffusion, apparent diffusion coefficient value and type of dynamic curve from perifocal lesion can benefit disease prognosis