Deep learning for intracranial aneurysm segmentation using CT angiography.

Objective.This study aimed to employ a two-stage deep learning method to accurately detect small aneurysms (4-10 mm in size) in computed tomography angiography images.Approach.This study included 956 patients from 6 hospitals and a public dataset obtained with 6 CT scanners from different manufacturers. The proposed method consists of two components: a lightweight and fast head region selection (HRS) algorithm and an adaptive 3D nnU-Net network, which is used as the main architecture for segmenting aneurysms. Segments generated by the deep neural network were compared with expert-generated manual segmentation results and assessed using Dice scores.MainResults.The area under the curve (AUC) exceeded 79% across all datasets. In particular, the precision and AUC reached 85.2% and 87.6%, respectively, on certain datasets. The experimental results demonstrated the promising performance of this approach, which reduced the inference time by more than 50% compared to direct inference without HRS.Significance.Compared with a model without HRS, the deep learning approach we developed can accurately segment aneurysms by automatically localizing brain regions and can accelerate aneurysm inference by more than 50%.

Toward human intervention-free clinical diagnosis of intracranial aneurysm via deep neural network.

Intracranial aneurysm (IA) is an enormous threat to human health, which often results in nontraumatic subarachnoid hemorrhage or dismal prognosis. Diagnosing IAs on commonly used computed tomographic angiography (CTA) examinations remains laborious and time consuming, leading to error-prone results in clinical practice, especially for small targets. In this study, we propose a fully automatic deep-learning model for IA segmentation that can be applied to CTA images. Our model, called Global Localization-based IA Network (GLIA-Net), can incorporate the global localization prior and generates the fine-grain three-dimensional segmentation. GLIA-Net is trained and evaluated on a big internal dataset (1,338 scans from six institutions) and two external datasets. Evaluations show that our model exhibits good tolerance to different settings and achieves superior performance to other models. A clinical experiment further demonstrates the clinical utility of our technique, which helps radiologists in the diagnosis of IAs.

Genome-Wide Identification and Characterization of the OPR Gene Family in Wheat (Triticum aestivum L.).

The 12-oxo-phytodienoic acid reductases (OPRs), which belong to the old yellow enzyme (OYE) family, are flavin mononucleotide (FMN)-dependent oxidoreductases with critical functions in plants. Despite the clear characteristics of growth and development, as well as the defense responses in Arabidopsis, tomato, rice, and maize, the potential roles of OPRs in wheat are not fully understood. Here, forty-eight putative OPR genes were found and classified into five subfamilies, with 6 in sub. I, 4 in sub. II, 33 in sub. III, 3 in sub. IV, and 2 in sub. V. Similar gene structures and conserved protein motifs of TaOPRs in wheat were identified in the same subfamilies. An analysis of cis-acting elements in promoters revealed that the functions of OPRs in wheat were mostly related to growth, development, hormones, biotic, and abiotic stresses. A total of 14 wheat OPR genes were identified as tandem duplicated genes, while 37 OPR genes were segmentally duplicated genes. The expression patterns of TaOPRs were tissue- and stress-specific, and the expression of TaOPRs could be regulated or induced by phytohormones and various stresses. Therefore, there were multiple wheat OPR genes, classified into five subfamilies, with functional diversification and specific expression patterns, and to our knowledge, this was the first study to systematically investigate the wheat OPR gene family. The findings not only provide a scientific foundation for the comprehensive understanding of the wheat OPR gene family, but could also be helpful for screening more candidate genes and breeding new varieties of wheat, with a high yield and stress resistance.