Fully automatic mpMRI analysis using deep learning predicts peritumoral glioblastoma infiltration and subsequent recurrence.

Glioblastoma (GBM) is most aggressive and common adult brain tumor. The standard treatments typically include maximal surgical resection, followed adjuvant radiotherapy and chemotherapy. However, the efficacy of these treatment is often limited, as tumor often infiltrate into the surrounding brain tissue, often extending beyond the radiologically defined margins. This infiltration contributes to the high recurrence rate and poor prognosis associated with GBM patients, necessitating advanced methods for early and accurate detection of tumor infiltration. Despite the great promise traditional supervised machine learning shows in predicting tumor infiltration beyond resectable margins, these methods are heavily reliant on expert-drawn Regions of Interest (ROIs), which are used to construct multi-variate models of different Magnetic Resonance (MR) signal characteristics associated with tumor infiltration. This process is both time consuming and resource intensive. Addressing this limitation, our study proposes a novel integration of fully automatic methods for generating ROIs with deep learning algorithms to create predictive maps of tumor infiltration. This approach uses pre-operative multi-parametric MRI (mpMRI) scans, encompassing T1, T1Gd, T2, T2-FLAIR, and ADC sequences, to fully leverage the knowledge from previously drawn ROIs. Subsequently, a patch based Convolutional Neural Network (CNN) model is trained on these automatically generated ROIs to predict areas of potential tumor infiltration. The performance of this model was evaluated using a leave-one-out cross-validation approach. Generated predictive maps binarized for comparison against post-recurrence mpMRI scans. The model demonstrates robust predictive capability, evidenced by the average cross-validated accuracy of 0.87, specificity of 0.88, and sensitivity of 0.90. Notably, the odds ratio of 8.62 indicates that regions identified as high-risk on the predictive map were significantly more likely to exhibit tumor recurrence than low-risk regions. The proposed method demonstrates that a fully automatic mpMRI analysis using deep learning can successfully predict tumor infiltration in peritumoral region for GBM patients while bypassing the intensive requirement for expert-drawn ROIs.

Deep Learning Segmentation, Visualization, and Automated 3D Assessment of Ciliary Body in 3D Ultrasound Biomicroscopy Images.

Purpose: This study aimed to develop a fully automated deep learning ciliary body segmentation and assessment approach in three-dimensional ultrasound biomicroscopy (3D-UBM) images. Methods: Each 3D-UBM eye volume was aligned to the optic axis via multiplanar reformatting. Ciliary muscle and processes were manually annotated, and Deeplab-v3+ models with different loss functions were trained to segment the ciliary body (ciliary muscle and processes) in both en face and radial images. Results: We trained and tested the models on 4320 radial and 3864 en face images from 12 cadaver eye volumes. Deep learning models trained on radial images with Dice loss achieved the highest mean F1-score (0.89) for ciliary body segmentation. For three-class segmentation (ciliary muscle, processes, and background), radial images with Dice loss achieved the highest mean F1-score (0.75 for the ciliary process and 0.82 for the ciliary muscle). Part of the ciliary muscle (10.9%) was misclassified as the ciliary process and vice versa, which occurred owing to the difficulty in differentiating the ciliary muscle-processes border, even by experts. Deep learning segmentation made further editing by experts at least seven times faster than a fully manual approach. In eight cadaver eyes, the average ciliary muscle, process, and body volumes were 56 ± 9, 43 ± 13, and 99 ± 18 mm3, respectively. The average surface area of the ciliary muscle, process, and body were 346 ± 45, 363 ± 83, and 709 ± 80 mm2, respectively. We performed transscleral cyclophotocoagulation in cadaver eyes to shrink the ciliary processes. Both manual and automated measurements from deep learning segmentation show a decrease in volume, surface area, and 360° cross-sectional area measurements. Conclusions: The proposed deep learning segmentation of the ciliary body and 3D measurements showed transscleral cyclophotocoagulation-related changes in the ciliary body. Translational Relevance: Automated ciliary body assessment using 3D-UBM has the translational potential for ophthalmic treatment planning and monitoring.

All-solution-processed PbS quantum dot solar modules.

A rapid increase in power conversion efficiencies in colloidal quantum dot (QD) solar cells has been achieved recently with lead sulphide (PbS) QDs by adapting a heterojunction architecture, which consists of small-area devices associated with a vacuum-deposited buffer layer with metal electrodes. The preparation of QD solar modules by low-cost solution processes is required to further increase the power-to-cost ratio. Herein we demonstrate all-solution-processed flexible PbS QD solar modules with a layer-by-layer architecture comprising polyethylene terephthalate (PET) substrate/indium tin oxide (ITO)/titanium oxide (TiO2)/PbS QD/poly(3-hexylthiophene) (P3HT)/poly(3,4-ethylenedioxythiophene) : poly(styrene sulfonate) (PEDOT : PSS)/Ag, with an active area of up to 30 cm(2), exhibiting a power conversion efficiency (PCE) of 1.3% under AM 1.5 conditions (PCE of 2.2% for a 1 cm(2) unit cell). Our approach affords trade-offs between power and the active area of the photovoltaic devices, which results in a low-cost power source, and which is scalable to larger areas.

Simple bar-coating process for large-area, high-performance organic field-effect transistors and ambipolar complementary integrated circuits.

Large-area polymer FET arrays and integrated circuits (ICs) are successfully demonstrated via a simple wire-bar-coating process. Both a highly crystalline conjugated polymer layer and very smooth insulating polymer layer are formed by a consecutive wire-bar-coating process on a 4-inch plastic substrate with a short processing time for application as the active and dielectric layers of OFET arrays and ICs.