A 2-Phase Merge Filter Approach to Computer-Aided Detection of Breast Tumors on 3-Dimensional Ultrasound Imaging.

OBJECTIVES: The role of image analysis in 3-dimensional (3D) automated breast ultrasound (ABUS) images is increasingly important because of its widespread use as a screening tool in whole-breast examinations. However, reviewing a large number of images acquired from ABUS is time-consuming and sometimes error prone. The aim of this study, therefore, was to develop an efficient computer-aided detection (CADe) algorithm to assist the review process. METHODS: The proposed CADe algorithm consisted of 4 major steps. First, initial tumor candidates were formed by extracting and merging hypoechoic square cells on 2-dimensional (2D) transverse images. Second, a feature-based classifier was then constructed using 2D features to filter out nontumor candidates. Third, the remaining 2D candidates were merged longitudinally into 3D masses. Finally, a 3D feature-based classifier was used to further filter out nontumor masses to obtain the final detected masses. The proposed method was validated with 176 passes of breast images acquired by an Acuson S2000 automated breast volume scanner (Siemens Medical Solutions USA, Inc., Malvern, PA), including 44 normal passes and 132 abnormal passes containing 162 proven lesions (79 benign and 83 malignant). RESULTS: The proposed CADe system could achieve overall sensitivity of 100% and 90% with 6.71 and 5.14 false-positives (FPs) per pass, respectively. Our results also showed that the average number of FPs per normal pass (7.16) was more than the number of FPs per abnormal pass (6.56) at 100% sensitivity. CONCLUSIONS: The proposed CADe system has a great potential for becoming a good companion tool with ABUS imaging by ensuring high sensitivity with a relatively small number of FPs.

A Variance-reduction Approach to Detection of the Thyroid-nodule Boundary on Ultrasound Images.

To perform computer-aided diagnosis of the thyroid nodules on ultrasound images, the location and boundary of nodules should be clearly defined. However, the identification of thyroid nodule boundary is a difficult issue due to the biological characteristics of the nodules, the physics and quality of ultrasound imaging, and the subjective factors and operating conditions of the operator. In this study, we propose a novel and semiautomatic method for detecting the boundary of thyroid nodule based on the Variance-Reduction (V-R) statistics without image preprocessing. The region of interest (ROI) is first automatically generated according to the initial inputs of the nodule's major and minor axes. The boundary candidate pixel points are then extracted by using the V-R statistics from the grayscale values of all pixel points in the ROI. Three filtering methods are further applied to eliminate the outlier pixel points to ensure that the remaining candidate pixel points are located on the nodule boundary. Finally, the remaining pixel points are smoothened and linked together to form the final boundary. The proposed method is validated with ultrasound images of 538 thyroid nodules, with manual delineation by experienced radiologist as gold standard. The effectiveness is evaluated and compared with previous publications using boundary error metrics and overlapping area metrics with the same data set. The results show that the normalized average mean boundary error is 1.02%, the true positive overlapping area ratio achieves 93.66% and false positive overlapping area ratio is limited to 7.68%. In conclusion, our proposed method is reliable and effective in detecting thyroid nodule boundary on ultrasound images.

Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images.

The presence of spread through air spaces (STASs) in early-stage lung adenocarcinoma is a significant prognostic factor associated with disease recurrence and poor outcomes. Although current STAS detection methods rely on pathological examinations, the advent of artificial intelligence (AI) offers opportunities for automated histopathological image analysis. This study developed a deep learning (DL) model for STAS prediction and investigated the correlation between the prediction results and patient outcomes. To develop the DL-based STAS prediction model, 1053 digital pathology whole-slide images (WSIs) from the competition dataset were enrolled in the training set, and 227 WSIs from the National Taiwan University Hospital were enrolled for external validation. A YOLOv5-based framework comprising preprocessing, candidate detection, false-positive reduction, and patient-based prediction was proposed for STAS prediction. The model achieved an area under the curve (AUC) of 0.83 in predicting STAS presence, with 72% accuracy, 81% sensitivity, and 63% specificity. Additionally, the DL model demonstrated a prognostic value in disease-free survival compared to that of pathological evaluation. These findings suggest that DL-based STAS prediction could serve as an adjunctive screening tool and facilitate clinical decision-making in patients with early-stage lung adenocarcinoma.

The Lipid Transfer Protein 1 from Nicotiana benthamiana Assists Bamboo mosaic virus Accumulation.

Host factors play a pivotal role in regulating virus infection. Uncovering the mechanism of how host factors are involved in virus infection could pave the way to defeat viral disease. In this study, we characterized a lipid transfer protein, designated NbLTP1 in Nicotiana benthamiana, which was downregulated after Bamboo mosaic virus (BaMV) inoculation. BaMV accumulation significantly decreased in NbLTP1-knockdown leaves and protoplasts compared with the controls. The subcellular localization of the NbLTP1-orange fluorescent protein (OFP) was mainly the extracellular matrix. However, when we removed the signal peptide (NbLTP1/ΔSP-OFP), most of the expressed protein targeted chloroplasts. Both NbLTP1-OFP and NbLTP1/ΔSP-OFP were localized in chloroplasts when we removed the cell wall. These results suggest that NbLTP1 may have a secondary targeting signal. Transient overexpression of NbLTP1 had no effect on BaMV accumulation, but that of NbLTP1/ΔSP significantly increased BaMV expression. NbLTP1 may be a positive regulator of BaMV accumulation especially when its expression is associated with chloroplasts, where BaMV replicates. The mutation was introduced to the predicted phosphorylation site to simulate the phosphorylated status, NbLTP/ΔSP/P(+), which could still assist BaMV accumulation. By contrast, a mutant lacking calmodulin-binding or simulates the phosphorylation-negative status could not support BaMV accumulation. The lipid-binding activity of LTP1 was reported to be associated with calmodulin-binding and phosphorylation, by which the C-terminus functional domain of NbLTP1 may play a critical role in BaMV accumulation.