Lee JY, et al. Korean J Radiol. 2021 May;22(5):840-860. https://doi.org/10.3348/kjr.2020.0578

Multi-label brain segmentation from brain magnetic resonance imaging (MRI) provides valuable structural information for most neurological analyses. Due to the complexity of the brain segmentation algorithm, it could delay the delivery of neuroimaging findings. Therefore, we introduce Split-Attention U-Net (SAU-Net), a convolutional neural network with skip pathways and a split-attention module that segments brain MRI scans. The proposed architecture employs split-attention blocks, skip pathways with pyramid levels, and evolving normalization layers. For efficient training, we performed pre-training and fine-tuning with the original and manually modified FreeSurfer labels, respectively. This learning strategy enables involvement of heterogeneous neuroimaging data in the training without the need for many manual annotations. Using nine evaluation datasets, we demonstrated that SAU-Net achieved better segmentation accuracy with better reliability that surpasses those of state-of-the-art methods. We believe that SAU-Net has excellent potential due to its robustness to neuroanatomical variability that would enable almost instantaneous access to accurate neuroimaging biomarkers and its swift processing runtime compared to other methods investigated.

Thermal ablation is considered a novel treatment alternative for benign thyroid nodules, with one of the most promising techniques being radiofrequency ablation (RFA). Considering the increasing use of thyroid RFA, some societies have proposed these clinical practice guidelines. Therefore, we systemically reviewed these guidelines for thyroid RFA and compared the similarities and differences between them to identify a standard treatment strategy that has been agreed upon by most societies. We searched studies with human participants that were published in the English language from 1 January 2000 to 2 August 2019 using MEDLINE and EMBASE databases. Studies that included clinical practice guideline of benign thyroid RFA were included. We extracted data on the indication, pre- and post-procedural evaluation, techniques, and informed consent. Of 83 studies, 4 studies were included, and 1 study was added after searching the bibliographies of these articles. Five studies were guidelines developed by the Korean Society of Thyroid Radiology (KSThR), Italian scientific societies, Italian minimally invasive treatment of the thyroid, National Institute for Health and Care Excellence in the United Kingdom, and four professional Austrian thyroid associations. Indications, pre- and post-procedural evaluation, and techniques were similar; however, there were differences in each guideline for RFA of the thyroid gland. Major categories are similar in guidelines. Establishment of international thyroid RFA guidelines for benign thyroid nodules needs in the future.

Lee MK, et al. Korean J Radiol. 2023 Jan;24(1):22-30. https://doi.org/10.3348/kjr.2022.0894

Objective Alzheimer’s disease (AD) is the most common type of dementia and the prevalence rapidly increased as the elderly population increased worldwide. In the contemporary model of AD, it is regarded as a disease continuum involving preclinical stage to severe dementia. For accurate diagnosis and disease monitoring, objective index reflecting structural change of brain is needed to correctly assess a patient’s severity of neurodegeneration independent from the patient’s clinical symptoms. The main aim of this paper is to develop a random forest (RF) algorithm-based prediction model of AD using structural magnetic resonance imaging (MRI).Methods We evaluated diagnostic accuracy and performance of our RF based prediction model using newly developed brain segmentation method compared with the Freesurfer’s which is a commonly used segmentation software.Results Our RF model showed high diagnostic accuracy for differentiating healthy controls from AD and mild cognitive impairment (MCI) using structural MRI, patient characteristics, and cognitive function (HC vs. AD 93.5%, AUC 0.99; HC vs. MCI 80.8%, AUC 0.88). Moreover, segmentation processing time of our algorithm (<5 minutes) was much shorter than of Freesurfer’s (6–8 hours).Conclusion Our RF model might be an effective automatic brain segmentation tool which can be easily applied in real clinical practice.