Feasibility Study on Automatic Interpretation of Radiation Dose Using Deep Learning Technique for Dicentric Chromosome Assay.

The interpretation of radiation dose is an important procedure for both radiological operators and persons who are exposed to background or artificial radiations. Dicentric chromosome assay (DCA) is one of the representative methods of dose estimation that discriminates the aberration in chromosomes modified by radiation. Despite the DCA-based automated radiation dose estimation methods proposed in previous studies, there are still limitations to the accuracy of dose estimation. In this study, a DCA-based automated dose estimation system using deep learning methods is proposed. The system is comprised of three stages. In the first stage, a classifier based on a deep learning technique is used for filtering the chromosome images that are not appropriate for use in distinguishing the chromosome; 99% filtering accuracy was achieved with 2,040 test images. In the second stage, the dicentric rate is evaluated by counting and identifying chromosomes based on the Feature Pyramid Network, which is one of the object detection algorithms based on deep learning architecture. The accuracies of the neural networks for counting and identifying chromosomes were estimated at over 97% and 90%, respectively. In the third stage, dose estimation is conducted using the dicentric rate and the dose-response curve. The accuracies of the system were estimated using two independent samples; absorbed doses ranging from 1- 4 Gy agreed well within a 99% confidential interval showing highest accuracy compared to those in previous studies. The goal of this study was to provide insights towards achieving complete automation of the radiation dose estimation, especially in the event of a large-scale radiation exposure incident.

Structural defects in a nanomesh of bulk MoS2 using an anodic aluminum oxide template for photoluminescence efficiency enhancement.

Two-dimensional (2D) materials beyond graphene have attracted considerable interest because of the zero bandgap drawbacks of graphene. Transition metal dichalcogenides (TMDs), such as MoS2 and WSe2, are the potential candidates for next 2D materials because atomically thin layers of TMDs exhibit unique and versatile electrical and optical properties. Although bulk TMDs materials have an indirect bandgap, an indirect-to-direct bandgap transition is observed in monolayers of TMDs (MoS2, WSe2, and MoSe2). Optical properties of TMD films can be improved by the introduction of structural defects. For example, large-area spatial tuning of the optical transition of bulk MoS2 films is achieved by using an anodic aluminum oxide (AAO) template to induce structural defects such as edge- and terrace-terminated defects in a nanomesh structure. Strong photoluminescence emission peaks with a band gap of 1.81 eV are observed, possibly because of radiative transition at the defect sites. This work shows that the AAO template lithography method has potential for the production of homogenous large-scale nanomesh structures for practical semiconductor processing applications in future MoS2-based electronic and optical devices.