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PURPOSE: Clinical risk scores are essential for predicting outcomes in stroke patients. The advancements in deep learning (DL) techniques provide opportunities to develop prediction applications using magnetic resonance (MR) images. We aimed to develop an MR-based DL imaging biomarker for predicting outcomes in acute ischemic stroke (AIS) and evaluate its additional benefit to current risk scores. METHOD: This study included 3338 AIS patients. We trained a DL model using deep neural network architectures on MR images and radiomics to predict poor functional outcomes at three months post-stroke. The DL model generated a DL score, which served as the DL imaging biomarker. We compared the predictive performance of this biomarker to five risk scores on a holdout test set. Additionally, we assessed whether incorporating the imaging biomarker into the risk scores improved the predictive performance. RESULTS: The DL imaging biomarker achieved an area under the receiver operating characteristic curve (AUC) of 0.788. The AUCs of the five studied risk scores were 0.789, 0.793, 0.804, 0.810, and 0.826, respectively. The imaging biomarker's predictive performance was comparable to four of the risk scores but inferior to one (p = 0.038). Adding the imaging biomarker to the risk scores improved the AUCs (p-values) to 0.831 (0.003), 0.825 (0.001), 0.834 (0.003), 0.836 (0.003), and 0.839 (0.177), respectively. The net reclassification improvement and integrated discrimination improvement indices also showed significant improvements (all p < 0.001). CONCLUSIONS: Using DL techniques to create an MR-based imaging biomarker is feasible and enhances the predictive ability of current risk scores.

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SLAM (simultaneous localization and mapping) plays a crucial role in autonomous robot navigation. A challenging aspect of visual SLAM systems is determining the 3D camera orientation of the motion trajectory. In this paper, we introduce an end-to-end network structure, InertialNet, which establishes the correlation between the image sequence and the IMU signals. Our network model is built upon inertial measurement learning and is employed to predict the camera's general motion pose. By incorporating an optical flow substructure, InertialNet is independent of the appearance of training sets and can be adapted to new environments. It maintains stable predictions even in the presence of image blur, changes in illumination, and low-texture scenes. In our experiments, we evaluated InertialNet on the public EuRoC dataset and our dataset, demonstrating its feasibility with faster training convergence and fewer model parameters for inertial measurement prediction.

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PURPOSE: To differentiate polypoidal choroidal vasculopathy (PCV) from choroidal neovascularization (CNV) and to determine the extent of PCV from fluorescein angiography (FA) using attention-based deep learning networks. METHODS: We build two deep learning networks for diagnosis of PCV using FA, one for detection and one for segmentation. Attention-gated convolutional neural network (AG-CNN) differentiates PCV from other types of wet age-related macular degeneration. Gradient-weighted class activation map (Grad-CAM) is generated to highlight important regions in the image for making the prediction, which offers explainability of the network. Attention-gated recurrent neural network (AG-PCVNet) for spatiotemporal prediction is applied for segmentation of PCV. RESULTS: AG-CNN is validated with a dataset containing 167 FA sequences of PCV and 70 FA sequences of CNV. AG-CNN achieves a classification accuracy of 82.80% at image-level, and 86.21% at patient-level for PCV. Grad-CAM shows that regions contributing to decision-making have on average 21.91% agreement with pathological regions identified by experts. AG-PCVNet is validated with 56 PCV sequences from the EVEREST-I study and achieves a balanced accuracy of 81.132% and dice score of 0.54. CONCLUSIONS: The developed software provides a means of performing detection and segmentation of PCV on FA images for the first time. This study is a promising step in changing the diagnostic procedure of PCV and therefore improving the detection rate of PCV using FA alone. TRANSLATIONAL RELEVANCE: The developed deep learning system enables early diagnosis of PCV using FA to assist the physician in choosing the best treatment for optimal visual prognosis.

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Pleural empyema is an inflammatory condition characterized by accumulation of pus inside the pleural cavity, which is usually followed by bacterial pneumonia. During the disease process, the pro-inflammatory and pro-fibrotic cytokines in the purulent pleural effusion cause proliferation of fibroblasts and deposition of extracellular matrix, which lead to fibrin deposition and fibrothorax. Urokinase instillation therapy through a chest drainage tube is frequently used for fibrinolysis in patients with empyema. However, urokinase treatment requires multiple instillation (2-3 times per day, for 4-8 days) and easily flows out from the chest drainage tube due to its high water solubility. In this in vitro study, we developed a thermo-responsive hydrogel based on poloxamer 407 (P407) combined with hyaluronic acid (HA) for optimal loading and release of urokinase. Our results show that the addition of HA to poloxamer gels provides a significantly more compact microstructure, with smaller pore sizes (**p < 0.001). The differential scanning calorimetry (DSC) profile revealed no influence on the micellization intensity of poloxamer gel by HA. The 25% poloxamer-based gel was significantly superior to the 23% poloxamer-based gel, with slower gel erosion when comparing the 16th hour residual gel weight of both gels (*p < 0.05; **p < 0.001). The 25% poloxamer-HA gel also exhibited a superior urokinase release profile and longer release time. A Fourier-transform infrared spectroscopy (FT-IR) study of the P407/HA hydrogel showed no chemical interactions between P407 and HA in the hydrogel system. The thermoresponsive P407/HA hydrogel may have a promising potential in the loading and delivery of hydrophilic drugs. On top of that, in vitro toxicity test of this combination demonstrates a lower toxicity.

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Indocyanine green (ICG) angiography is an imaging method for doctors to observe choroidal abnormalities in human eyes. The ICG angiograms typically exhibit inhomogeneous illumination, which poses serious difficulties for the development of computer-aided diagnostic tools. In this paper, we propose a novel illumination normalization method to alleviate the inhomogeneous illumination in ICG video angiograms. In particular, we first align the viewpoint of the input ICG video angiogram using an image registration method. Then, we acquire temporal information using time-dependent intrinsic image and compute the corresponding illumination image. Finally, we correct inhomogeneous illumination from the illumination image by estimating contrast and luminosity distortion. We have conducted extensive evaluation using ICG video angiograms of 60 patients. Two video quality assessment methods are utilized to evaluate the performance of our proposed illumination normalization method. The results show that our proposed method can help improve the visual quality of ICG video angiogram. Visual evaluation by a human expert also confirms that our method yields better illumination normalization results.

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PURPOSE: To develop a computer-aided diagnostic tool for automated detection and quantification of polypoidal regions in indocyanine green angiography (ICGA) images. METHODS: The ICGA sequences of 59 polypoidal choroidal vasculopathy (PCV) treatment-naïve patients from five Asian countries (Hong Kong, Singapore, South Korea, Taiwan, and Thailand) were provided by the EVEREST study. The ground truth was provided by the reading center for the presence of polypoidal regions. The proposed detection algorithm used both temporal and spatial features to characterize the severity of polypoidal lesions in ICGA sequences. Leave-one-out cross validation was carried out so that each patient was used once as the validation sample. For each patient, a fixed detection threshold of 0.5 on the severity was applied to obtain sensitivity, specificity, and balanced accuracy with respect to the ground truth. RESULTS: Our system achieved an average accuracy of 0.9126 (sensitivity = 0.9125, specificity = 0.9127) for detection of polyps in the 59 ICGA sequences. Among the total of 222 features extracted from ICGA sequence, the spatial variances exhibited best discriminative power in distinguishing between polyp and nonpolyp regions. The results also indicated the importance of combining spatial and temporal features to further improve detection accuracy. CONCLUSIONS: The developed software provided a means of detecting and quantifying polypoidal regions in ICGA images for the first time. TRANSLATIONAL RELEVANCE: This preliminary study demonstrated a computer-aided diagnostic tool, which enables objective evaluation of PCV and its progression. Ophthalmologists can easily visualize the polypoidal regions and obtain quantitative information about polyps by using the proposed system.

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Acquired nonmalignant tracheoesophageal fistula (TEF) is a rare clinical condition with multiple etiologies, although post-intubation injury is the most common cause. TEFs can be fatal if left untreated due to devastating pulmonary complications caused by tracheobronchial contamination and poor nutrition. Herein, we present a case of complete healing of a post-intubation TEF under conservative treatment in a ventilator-dependent patient, and review previous studies regarding the treatment of acquired nonmalignant TEFs.

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PURPOSE: To develop a real-time automatic method for tracking implanted radiographic markers in low-contrast cine-MV patient images used in image-guided radiation therapy (IGRT). METHODS: Intrafraction motion tracking using radiotherapy beam-line MV images have gained some attention recently in IGRT because no additional imaging dose is introduced. However, MV images have much lower contrast than kV images, therefore a robust and automatic algorithm for marker detection in MV images is a prerequisite. Previous marker detection methods are all based on template matching or its derivatives. Template matching needs to match object shape that changes significantly for different implantation and projection angle. While these methods require a large number of templates to cover various situations, they are often forced to use a smaller number of templates to reduce the computation load because their methods all require exhaustive search in the region of interest. The authors solve this problem by synergetic use of modern but well-tested computer vision and artificial intelligence techniques; specifically the authors detect implanted markers utilizing discriminant analysis for initialization and use mean-shift feature space analysis for sequential tracking. This novel approach avoids exhaustive search by exploiting the temporal correlation between consecutive frames and makes it possible to perform more sophisticated detection at the beginning to improve the accuracy, followed by ultrafast sequential tracking after the initialization. The method was evaluated and validated using 1149 cine-MV images from two prostate IGRT patients and compared with manual marker detection results from six researchers. The average of the manual detection results is considered as the ground truth for comparisons. RESULTS: The average root-mean-square errors of our real-time automatic tracking method from the ground truth are 1.9 and 2.1 pixels for the two patients (0.26 mm/pixel). The standard deviations of the results from the 6 researchers are 2.3 and 2.6 pixels. The proposed framework takes about 128 ms to detect four markers in the first MV images and about 23 ms to track these markers in each of the subsequent images. CONCLUSIONS: The unified framework for tracking of multiple markers presented here can achieve marker detection accuracy similar to manual detection even in low-contrast cine-MV images. It can cope with shape deformations of fiducial markers at different gantry angles. The fast processing speed reduces the image processing portion of the system latency, therefore can improve the performance of real-time motion compensation.

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Motivated by the goals of automatically extracting vessel segments and constructing retinal vascular trees with anatomical realism, this paper presents and analyses an algorithm that combines vessel segmentation and grouping of the extracted vessel segments. The proposed method aims to restore the topology of the vascular trees with anatomical realism for clinical studies and diagnosis of retinal vascular diseases, which manifest abnormalities in either venous and/or arterial vascular systems. Vessel segments are grouped using extended Kalman filter which takes into account continuities in curvature, width, and intensity changes at the bifurcation or crossover point. At a junction, the proposed method applies the minimum-cost matching algorithm to resolve the conflict in grouping due to error in tracing. The system was trained with 20 images from the DRIVE dataset, and tested using the remaining 20 images. The dataset contained a mixture of normal and pathological images. In addition, six pathological fluorescein angiogram sequences were also included in this study. The results were compared against the groundtruth images provided by a physician, achieving average success rates of 88.79% and 90.09%, respectively.

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BACKGROUND: Positron emission tomography combined with computed tomography (PET-CT) is important in the assessment and workup of lung cancer staging. However, inconsistencies between clinical image results obtained and pathologic findings of surgical specimens are still very common, particularly in patients with clinical early stage lung cancer. We sought to clarify the role of PET-CT in predicting mediastinal lymph node status preoperatively in clinical early stage lung cancer patients. METHODS: The cases were collected retrospectively from January 2008 to February 2009. All patients were good surgical candidates, and clinically early-stage during the pre-op evaluation, which included CT, PET scan, and cardiopulmonary tests. All patients underwent surgery, with complete pathological evaluation of mediastinal lymph node (LNs). The pathological status and PET Standardized uptake value (SUV)(max) of mediastinal LNs were collected to calculate the ROC curve, and to determine the best cut-off value of PET SUV(max). Other cofactors, including sex, tumor size, tumor SUV(max), histology type, and lobar distribution, were analyzed utilizing correlation study, Chi-square test, and t-test for significance. RESULTS: A total of 83 patients were enrolled into the study. The majority of the cases were in pathological early stage (Stage I: 67.5%, Stage II: 12%). The cut-off point of mediastinal LN SUV(max) was 1.6 calculated by receiver operating characteristic (ROC) curve (sensitivity: 40%, specificity: 88.7%, negative predictive rate: 95.1%). The hilar LN SUV(max) was found to have a poor correlation to the final pathologic status of hilar nodes with insignificant p value (0.487). Tumor SUV(max) and increased hilar LN uptake (SUV(max) >  2.0) were found to be significantly correlated with the pathologic status of mediastinal LNs. The false positive rates by PET-CT scan in N1 and N2 nodes were 70% and 78%, respectively, primarily due to inflammatory process (as anthracosis the leading cause). CONCLUSION: Integrated PET-CT is a useful tool for predicting the negativity of mediastinal LN status pre-operatively in clinically early stage (Stages I and II) lung cancer but may be relatively inaccurate in predicting hilar LN status and largely confounded by false positives caused by inflammatory process.

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PURPOSE: To provide a computer-aided visualization tool for accurate diagnosis and quantification of choroidal neovascularization (CNV) on the basis of fluorescence leakage characteristics. METHODS: All image frames of a fluorescein angiography (FA) sequence are first aligned and mapped to a global space. To automatically determine the severity of each pixel in the global space and hence the extent of CNV, the system matches the intensity variation of each set of spatially corresponding pixels across the sequence with the targeted leakage pattern, learned from a sampled population graded by a retina specialist. The learning strategy, known as the AdaBoost algorithm, has 12 classifiers for 12 features that summarize the variation in fluorescence intensity over time. Given a new sequence, the severity map image is generated using the contribution scores of the 12 classifiers. Initialized with points of low and high severity, regions of CNV are delineated using the random walk algorithm. RESULTS: A dataset of 33 FA sequences of classic CNV showed the average accuracy of CNV delineation to be 83.26%. In addition, the 30- to 60-second interval provided the most reliable information for differentiating CNV from the background. Using eight sequences of multiple visits of four patients for evaluation of the postphotodynamic therapy (PDT), the statistics derived from the segmented regions correlate closely with the clinical observed changes. CONCLUSIONS: The clinician can easily visualize the temporal characteristics of CNV fluorescence leakage using the severity map, which is a two-dimensional summary of a complete FA sequence. The computer-aided tool allows objective evaluation and computation of statistical data from the automatic delineation for surgical assessment.

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Tumor-to-tumor metastasis is a rare occurrence. The most frequent metastatic donor is lung cancer, and the most frequent recipient is renal cell carcinoma. Here we present a patient in whom lung cancer served as a metastatic recipient and maxillary sinus adenoid cystic carcinoma acted as a metastatic donor. This patient has double primary lung cancer, which increased the complexity of this case.

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This paper presents a novel 3-D multiregion face recognition algorithm that consists of new geometric summation invariant features and an optimal linear feature fusion method. A summation invariant, which captures local characteristics of a facial surface, is extracted from multiple subregions of a 3-D range image as the discriminative features. Similarity scores between two range images are calculated from the selected subregions. A novel fusion method that is based on a linear discriminant analysis is developed to maximize the verification rate by a weighted combination of these similarity scores. Experiments on the Face Recognition Grand Challenge V2.0 dataset show that this new algorithm improves the recognition performance significantly in the presence of facial expressions.

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