Neurofibromatosis type 1 (NF1) is one of the most common genetic disorders. Gastrointestinal manifestations of NF-1 are seldom thought of in routine clinical practice and might thus be significantly under-recognised. Their heterogeneous spectrum ranges from localised microscopic proliferative lesions to grossly recognizable mass-forming neurofibromas, neuroendocrine and gastrointestinal stromal tumours (GIST). The aim of this study is discussing the imaging evaluation and characterisation of the abdomen lesions in patients with NF1.Teaching Points• Neurofibromatosis type (NF-1) is one of the most common single gene disorders.• Every organ system can be involved and intra-abdominal manifestations are underestimated.• The NF1 abdominal manifestations comprehend five categories of tumours.• Neurogenic tumours including with neurofibromas are the most common type.• Early diagnosis of abdominal manifestations of NF-1 based on imaging patterns is necessary for appropriate treatment to avoid serious organic complications related to tumour mass.

The segmentation of the retinal vascular tree presents a major step for detecting ocular pathologies. The clinical context expects higher segmentation performance with a reduced processing time. For higher accurate segmentation, several automated methods have been based on Deep Learning (DL) networks. However, the used convolutional layers bring to a higher computational complexity and so for execution times. For such need, this work presents a new DL based method for retinal vessel tree segmentation. Our main contribution consists in suggesting a new U-form DL architecture using lightweight convolution blocks in order to preserve a higher segmentation performance while reducing the computational complexity. As a second main contribution, preprocessing and data augmentation steps are proposed with respect to the retinal image and blood vessel characteristics. The proposed method is tested on DRIVE and STARE databases, which can achieve a better trade-off between the retinal blood vessel detection rate and the detection time with average accuracy of 0.978 and 0.98 in 0.59s and 0.48s per fundus image on GPU NVIDIA GTX 980 platforms, respectively for DRIVE and STARE database fundus images.

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BackgroundThe sequence of Q, R, and S peaks (QRS) complex detection is a crucial procedure in electrocardiogram (ECG) processing and analysis. We propose a novel approach for QRS complex detection based on the deterministic finite automata with the addition of some constraints. This paper confirms that regular grammar is useful for extracting QRS complexes and interpreting normalized ECG signals. A QRS is assimilated to a pair of adjacent peaks which meet certain criteria of standard deviation and duration.ResultsThe proposed method was applied on several kinds of ECG signals issued from the standard MIT-BIH arrhythmia database. A total of 48 signals were used. For an input signal, several parameters were determined, such as QRS durations, RR distances, and the peaks’ amplitudes. σRR and σQRS parameters were added to quantify the regularity of RR distances and QRS durations, respectively. The sensitivity rate of the suggested method was 99.74% and the specificity rate was 99.86%. Moreover, the sensitivity and the specificity rates variations according to the Signal-to-Noise Ratio were performed.ConclusionsRegular grammar with the addition of some constraints and deterministic automata proved functional for ECG signals diagnosis. Compared to statistical methods, the use of grammar provides satisfactory and competitive results and indices that are comparable to or even better than those cited in the literature.