Lung cancer tends to be detected at an advanced stage, resulting in a high patient mortality rate. Thus, much recent research has focused on early disease detection Bronchoscopy is the procedure of choice for an effective noninvasive way of detecting early manifestations (bronchial lesions) of lung cancer. In particular, autofluorescence bronchoscopy (AFB) discriminates the autofluorescence properties of normal (green) and diseased tissue (reddish brown) with different colors. Because recent studies show AFB's high sensitivity in searching lesions, it has become a potentially pivotal method in bronchoscopic airway exams. Unfortunately, manual inspection of AFB video is extremely tedious and error prone, while limited effort has been expended toward potentially more robust automatic AFB lesion analysis. We propose a real-time (processing throughput of 27 frames/sec) deep-learning architecture dubbed ESFPNet for accurate segmentation and robust detection of bronchial lesions in AFB video streams. The architecture features an encoder structure that exploits pretrained Mix Transformer (MiT) encoders and an efficient stage-wise feature pyramid (ESFP) decoder structure. Segmentation results from the AFB airway-exam videos of 20 lung cancer patients indicate that our approach gives a mean Dice index = 0.756 and an average Intersection of Union = 0.624, results that are superior to those generated by other recent architectures. Thus, ESFPNet gives the physician a potential tool for confident real-time lesion segmentation and detection during a live bronchoscopic airway exam. Moreover, our model shows promising potential applicability to other domains, as evidenced by its state-of-the-art (SOTA) performance on the CVC-ClinicDB, ETIS-LaribPolypDB datasets, and superior performance on the Kvasir, CVC-ColonDB datasets.
Oxygenation concentration of tissue is an important factor in culturing stem cells and in studying the therapy response of cancer cells. The hypoxia bone marrow is the site to harbor cancer cells. Thus, direct high-resolution measurements of molecular O2 would provide powerful means of monitoring cultured stem cells and therapied cancer cells. We proposed an imaging approach to measure oxygenation concentration in deep tissues, based on the XLCT, with combined strengths of high chemical sensitivity and high spatial resolution. We have developed different biosensing films for oxygenation measurements and tested these films with X-ray luminescent experiments. We have also performed phantom experiments with multiple targets to validate the XLCT imaging system with measurements at two channels.
1000 fps HSA enables visualization of flow details, which may be important in accurately guiding interventional procedures; however, single-plane imaging may lack clear visualization of vessel geometry and flow detail. The previously presented high-speed orthogonal biplane imaging may overcome these limitations but may still result in foreshortening of vessel morphology. In certain morphologies, acquiring two non-orthogonal biplane projections at multiple angles can provide better flow detail rather than a standard orthogonal biplane acquisition. Flow studies of aneurysm models were performed, where simultaneous biplane acquisitions at various angles separating the two detector views allowed for better evaluation of morphology and flow. 3D-printed, patient-specific internal carotid artery aneurysm models were imaged with various non-orthogonal angles between the two high-speed photon-counting detectors (7.5 cm x 5 cm FOV) to provide frame-correlated simultaneous 1000-fps image sequences. Fluid dynamics were visualized in multi-angled planes of each model using automated injections of iodine contrast media. The resulting dual simultaneous frame-correlated 1000-fps acquisitions from multiple planes of each aneurysm model provided improved visualization of complex aneurysm geometries and flow streamlines. Multi-angled biplane acquisitions with frame correlation allows for further understanding of aneurysm morphology and flow details: additionally, the ability to recover fluid dynamics at depth enables accurate analysis of 3D flow streamlines, and it is expected that multiple-planar views will enable better volumetric flow visualization and quantification. Such better visualization has the potential to improve interventional procedures.
Purpose: To advance the development of radiomic models of bone quality using the recently introduced Ultra-High Resolution CT (UHR CT), we investigate inter-scan reproducibility of trabecular bone texture features to spatially-variant azimuthal and radial blurs associated with focal spot elongation and gantry rotation. Methods: The UHR CT system features 250x250 μm detector pixels and an x-ray source with a 0.4x0.5 mm focal spot. Visualization of details down to ~150 μm has been reported for this device. A cadaveric femur was imaged on UHR CT at three radial locations within the field-of-view: 0 cm (isocenter), 9 cm from the isocenter, and 18 cm from the isocenter; we expect the non-stationary blurs to worsen with increasing radial displacement. Gray level cooccurrence (GLCM) and gray level run length (GLRLM) texture features were extracted from 237 trabecular regions of interest (ROIs, 5 cm diameter) placed at corresponding locations in the femoral head in scans obtained at the different shifts. We evaluated concordance correlation coefficient (CCC) between texture features at 0 cm (reference) and at 9 cm and 18 cm. We also investigated whether the spatially-variant blurs affect K-means clustering of trabecular bone ROIs based on their texture features. Results: The average CCCs (against the 0 cm reference) for GLCM and GLRM features were ~0.7 at 9 cm. At 18 cm, the average CCCs were reduced to ~0.17 for GLCM and ~0.26 for GLRM. The non-stationary blurs are incorporated in radiomic features of cancellous bone, leading to inconsistencies in clustering of trabecular ROIs between different radial locations: an intersection-over-union overlap of corresponding (most similar) clusters between 0 cm and 9 cm shift was >70%, but dropped to <60% for the majority of corresponding clusters between 0 cm and 18 cm shift. Conclusion: Non-stationary CT system blurs reduce inter-scan reproducibility of texture features of trabecular bone in UHR CT, especially for locations <15 cm from the isocenter. Radiomic models of bone quality derived from UHR CT measurements at isocenter might need to be revised before application in peripheral body sites such as the hips.
Tumor-node-metastasis (TNM) classification for lung cancer is essential for appropriate treatment strategies and has been used widely in the investigation and treatment of this cancer. In TNM classification, N descriptors are one of the most important prognostic indicators and are determined by the metastatic lymph node stations. Therefore, accurate classification of lymph nodes is crucial. Thoracic contrast-enhanced Computed Tomography (CT) images represent the gold-standard modality. However, manual segmentation and classification of lymph nodes are challenges that arise from the relatively similar attenuation between lymph nodes and surrounding structures. Recent progress of convolutional neural network (CNN) has spawned research on mediastinal lymph nodes segmentation on chest CT images using CNNs. However, the previous CNN-based method did not consider the relationship between airways and lymph node locations for segmenting the thoracic N1 lymph nodes group. In this study, we investigate whether distance maps based on tracheobronchial labeling can represent the anatomy properties of the N1 lymph nodes group in volumetric CT images using the NIH open-source dataset.
