Automated Bolus Detection in Videofluoroscopic Images of Swallowing Using Mask-RCNN

Analysis of the Diagnosis Model of Peripheral Non-Small-Cell Lung Cancer under Computed Tomography Images

This study aimed to explore the effect of deep learning models on lung CT image lung parenchymal segmentation (LPS) and the application value of CT image texture features in the diagnosis of peripheral non-small-cell lung cancer (NSCLC). Data of peripheral lung cancer (PLC) patients was collected retrospectively and was divided into peripheral SCLC group and peripheral NSCLC group according to the pathological examination results, ResNet50 model and feature pyramid network (FPN) algorithm were undertaken to improve the Mask-RCNN model, and after the MaZda software extracted the texture features of the CT images of PLC patients, the Fisher coefficient was used to reduce the dimensionality, and the texture features of the CT images were analyzed and compared. The results showed that the average Dice coefficients of the 2D CH algorithm, Faster-RCNN, Mask-RCNN, and the algorithm proposed in the validation set were 0.882, 0.953, 0.961, and 0.986, respectively. The accuracy rates were 88.3%, 93.5%, 94.4%, and 97.2%. The average segmentation speeds in lung CT images were 0.289 s/sheet, 0.115 s/sheet, 0.108 s/sheet, and 0.089 s/sheet. The improved deep learning model showed higher accuracy, better robustness, and faster speed than other algorithms in the LPS of CT images. In summary, deep learning can achieve the LPS of CT images and show excellent segmentation efficiency. The texture parameters of GLCM in CT images have excellent differential diagnosis performance for NSCLC and SCLC and potential clinical application value.

Deep Learning-Based Chest CT Image Features in Diagnosis of Lung Cancer

This study was to evaluate the diagnostic value of deep learning-optimized chest CT in the patients with lung cancer. 90 patients who were diagnosed with lung cancer by surgery or puncture in hospital were selected as the research subjects. The Mask Region Convolutional Neural Network (Mask-RCNN) model was a typical end-to-end image segmentation model, and Dual Path Network (DPN) was used in nodule detection. The results showed that the accuracy of DPN algorithm model in detecting lung lesions in lung cancer patients was 88.74%, the accuracy of CT diagnosis of lung cancer was 88.37%, the sensitivity was 82.91%, and the specificity was 87.43%. Deep learning-based CT examination combined with serum tumor detection, factoring into Neurospecific enolase (N S E), cytokeratin 19 fragment (CYFRA21), Carcinoembryonic antigen (CEA), and squamous cell carcinoma (SCC) antigen, improved the accuracy to 97.94%, the sensitivity to 98.12%, and the specificity to 100%, all showing significant differences (P < 0.05). In conclusion, this study provides a scientific basis for improving the diagnostic efficiency of CT imaging in lung cancer and theoretical support for subsequent lung cancer diagnosis and treatment.

The effect of time on the automated detection of the pharyngeal phase in videofluoroscopic swallowing studies

Convolutional Neural Networks (CNNs) have recently been proposed to automatically detect the pharyngeal phase in videofluoroscopic swallowing studies (VFSS). However, there is a lack of consensus regarding the best algorithmic strategy to adopt for segmenting this important yet rapid phase of the swallow. Moreover, additional information is needed to understand how small the detection error should be, in view of translating this approach for use in clinical practice. In this manuscript we compare multiple CNN-based algorithms for detecting the pharyngeal phase in VFSS bolus-level clips, specifically looking at 2DCNN and 3DCNN approaches with different temporal windows as input. Our results showed that a 2DCNN analysis on 3-frame windows outperformed both frame-by-frame approaches and 3DCNNs. We also demonstrated that the detection accuracy of the pharyngeal phase is very close to the clinical gold standard (i.e., trained clinical raters). These results demonstrate the feasibility of deep learning-based algorithms for developing intelligent approaches to automatically support clinicians in the analysis of VFSS data.Clinical relevance- Accurate and reliable segmentation of the pharyngeal phase will support clinicians by reducing the time needed for rating VFSS data. Moreover, automatic detection of this phase can be seen as a foundation for building novel and intelligent approaches to detect clinical features of interest in VFSS, such as the presence of penetration-aspiration.