Nighttime color constancy using robust gray pixels.

Color constancy is a basic step for achieving stable color perception in both biological visual systems and the image signal processing (ISP) pipeline of cameras. So far, there have been numerous computational models of color constancy that focus on scenes under normal light conditions but are less concerned with nighttime scenes. Compared with daytime scenes, nighttime scenes usually suffer from relatively higher-level noise and insufficient lighting, which usually degrade the performance of color constancy methods designed for scenes under normal light. In addition, there is a lack of nighttime color constancy datasets, limiting the development of relevant methods. In this paper, based on the gray-pixel-based color constancy methods, we propose a robust gray pixel (RGP) detection method by carefully designing the computation of illuminant-invariant measures (IIMs) from a given color-biased nighttime image. In addition, to evaluate the proposed method, a new dataset that contains 513 nighttime images and corresponding ground-truth illuminants was collected. We believe this dataset is a useful supplement to the field of color constancy. Finally, experimental results show that the proposed method achieves superior performance to statistics-based methods. In addition, the proposed method was also compared with recent deep-learning methods for nighttime color constancy, and the results show the method's advantages in cross-validation among different datasets.

Haze removal with channel-wise scattering coefficient awareness based on grey pixels.

Before being captured by observers, the information carried by light may be attenuated by the transmission medium. According to the atmospheric scattering model, this attenuation is wavelength-dependent and increases with distance. However, most existing haze removal methods ignore this wavelength dependency and therefore cannot handle well the color distortions caused by it. To solve this problem, we propose a scattering coefficient awareness method based on the image formation model. The proposed method first makes an initial transmission estimation by the dark channel prior and then calculates the scattering coefficient ratios based on the initial transmission map and the grey pixels in the image. After that, fine transmission maps in RGB channels are calculated from these ratios and compensated for in sky areas. A global correction is also applied to eliminate the color bias induced by the light source before the final output. Qualitatively and quantitatively compared on synthetic and real images against state-of-the-art methods, the proposed method provides better results for the scenes with either white fog or colorized haze.

A computational model for gestalt proximity principle on dot patterns and beyond.

The human visual system has the ability to group parts of stimuli into larger, inherently structured units. In this article, a computational model inspired by tolerance space theory simulating the human perceptual grouping of dot patterns is proposed. Tolerance space theory introduces a tolerance relation to a discrete set to formulate the continuity of the discrete patterns. The model proposed herein includes one- and two-reach methods based on the assumption that dot patterns can be represented in the proposed extended tolerance space (ETS). Both methods are used to construct a ratio neighborhood graph (RANG), calculate tolerance from the diagram, compute the new RANG, and then rebuild continuous structures from the new RANG with a combinatorial procedure. Experiments are conducted to show the high consistency of the proposed model with human perception for various shapes of dot patterns, its ability to simulate Gestalt proximity and similarity principles, and its potential application in computer vision. In addition, the close relationship of the proposed model with the Pure Distance Law is comprehensively revealed, and the hierarchical representation of perceptual grouping is simulated with an adaptation of the proposed model based on the ETS.

Retina inspired tone mapping method for high dynamic range images.

The limited dynamic range of regular screens restricts the display of high dynamic range (HDR) images. Inspired by retinal processing mechanisms, we propose a tone mapping method to address this problem. In the retina, horizontal cells (HCs) adaptively adjust their receptive field (RF) size based on the local stimuli to regulate the visual signals absorbed by photoreceptors. Using this adaptive mechanism, the proposed method compresses the dynamic range locally in different regions, and has the capability of avoiding halo artifacts around the edges of high luminance contrast. Moreover, the proposed method introduces the center-surround antagonistic RF structure of bipolar cells (BCs) to enhance the local contrast and details. Extensive experiments show that the proposed method performs robustly well on a wide variety of images, providing competitive results against the state-of-the-art methods in terms of visual inspection, objective metrics and observer scores.

INHBA overexpression indicates poor prognosis in urothelial carcinoma of urinary bladder and upper tract.

BACKGROUND: Urothelial carcinoma (UC) originating from the bladder (UBUC) and upper urinary tract (UTUC) is the most common type of urinary tract tumor. While its pathogenesis remains obscured. Computerizing a published transcriptomic database of UBUC (GSE31684), we identified Inhibin, Beta A (INHBA) as the most significant upregulated gene associated with tumor progression among those associated with growth factor activity (GO:0008083). We therefore analyzed the clinicopathological significance of INHBA expression in UC. DESIGN: QuantiGene assay was used to detect INHBA transcript level in 36 UTUCs and 30 UBUCs. Immunohistochemistry evaluated by H-score was used to determine INHBA protein expression in 340 UTUCs and 296 UBUCs. INHBA expression was correlated with clinicopathological features and disease-specific survival (DSS) and metastasis-free survival (MeFS). RESULTS: Increments of INHBA transcript level was associated with higher pT status in both UTUC and UBUC. INHBA protein overexpression was significantly associated with advanced clinicopathological features in both groups of UC. INHBA overexpression significantly implied inferior DSS (UTUC, P = 0.002; UBUC, P = 0.005) and MeFS (UTUC and UBUC, both P < 0.001) in multivariate analysis. CONCLUSION: INHBA overexpression implies adverse clinical outcomes for UC, justifying it is a potential prognostic biomarker and a novel therapeutic target in UC.

Retinal Layer Segmentation in OCT Images With Boundary Regression and Feature Polarization.

The geometry of retinal layers is an important imaging feature for the diagnosis of some ophthalmic diseases. In recent years, retinal layer segmentation methods for optical coherence tomography (OCT) images have emerged one after another, and huge progress has been achieved. However, challenges due to interference factors such as noise, blurring, fundus effusion, and tissue artifacts remain in existing methods, primarily manifesting as intra-layer false positives and inter-layer boundary deviation. To solve these problems, we propose a method called Tightly combined Cross-Convolution and Transformer with Boundary regression and feature Polarization (TCCT-BP). This method uses a hybrid architecture of CNN and lightweight Transformer to improve the perception of retinal layers. In addition, a feature grouping and sampling method and the corresponding polarization loss function are designed to maximize the differentiation of the feature vectors of different retinal layers, and a boundary regression loss function is devised to constrain the retinal boundary distribution for a better fit to the ground truth. Extensive experiments on four benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance in dealing with problems of false positives and boundary distortion. The proposed method ranked first in the OCT Layer Segmentation task of GOALS challenge held by MICCAI 2022. The source code is available at https://www.github.com/tyb311/TCCT.

Basal actomyosin pulses expand epithelium coordinating cell flattening and tissue elongation.

Actomyosin networks constrict cell area and junctions to alter cell and tissue shape. However, during cell expansion under mechanical stress, actomyosin networks are strengthened and polarized to relax stress. Thus, cells face a conflicting situation between the enhanced actomyosin contractile properties and the expansion behaviour of the cell or tissue. To address this paradoxical situation, we study late Drosophila oogenesis and reveal an unusual epithelial expansion wave behaviour. Mechanistically, Rac1 and Rho1 integrate basal pulsatile actomyosin networks with ruffles and focal adhesions to increase and then stabilize basal area of epithelial cells allowing their flattening and elongation. This epithelial expansion behaviour bridges cell changes to oocyte growth and extension, while oocyte growth in turn deforms the epithelium to drive cell spreading. Basal pulsatile actomyosin networks exhibit non-contractile mechanics, non-linear structures and F-actin/Myosin-II spatiotemporal signal separation, implicating unreported expanding properties. Biophysical modelling incorporating these expanding properties well simulates epithelial cell expansion waves. Our work thus highlights actomyosin expanding properties as a key mechanism driving tissue morphogenesis.

A lightweight network guided with differential matched filtering for retinal vessel segmentation.

The geometric morphology of retinal vessels reflects the state of cardiovascular health, and fundus images are important reference materials for ophthalmologists. Great progress has been made in automated vessel segmentation, but few studies have focused on thin vessel breakage and false-positives in areas with lesions or low contrast. In this work, we propose a new network, differential matched filtering guided attention UNet (DMF-AU), to address these issues, incorporating a differential matched filtering layer, feature anisotropic attention, and a multiscale consistency constrained backbone to perform thin vessel segmentation. The differential matched filtering is used for the early identification of locally linear vessels, and the resulting rough vessel map guides the backbone to learn vascular details. Feature anisotropic attention reinforces the vessel features of spatial linearity at each stage of the model. Multiscale constraints reduce the loss of vessel information while pooling within large receptive fields. In tests on multiple classical datasets, the proposed model performed well compared with other algorithms on several specially designed criteria for vessel segmentation. DMF-AU is a high-performance, lightweight vessel segmentation model. The source code is at https://github.com/tyb311/DMF-AU.

Retinal Vessel Segmentation With Skeletal Prior and Contrastive Loss.

The morphology of retinal vessels is closely associated with many kinds of ophthalmic diseases. Although huge progress in retinal vessel segmentation has been achieved with the advancement of deep learning, some challenging issues remain. For example, vessels can be disturbed or covered by other components presented in the retina (such as optic disc or lesions). Moreover, some thin vessels are also easily missed by current methods. In addition, existing fundus image datasets are generally tiny, due to the difficulty of vessel labeling. In this work, a new network called SkelCon is proposed to deal with these problems by introducing skeletal prior and contrastive loss. A skeleton fitting module is developed to preserve the morphology of the vessels and improve the completeness and continuity of thin vessels. A contrastive loss is employed to enhance the discrimination between vessels and background. In addition, a new data augmentation method is proposed to enrich the training samples and improve the robustness of the proposed model. Extensive validations were performed on several popular datasets (DRIVE, STARE, CHASE, and HRF), recently developed datasets (UoA-DR, IOSTAR, and RC-SLO), and some challenging clinical images (from RFMiD and JSIEC39 datasets). In addition, some specially designed metrics for vessel segmentation, including connectivity, overlapping area, consistency of vessel length, revised sensitivity, specificity, and accuracy were used for quantitative evaluation. The experimental results show that, the proposed model achieves state-of-the-art performance and significantly outperforms compared methods when extracting thin vessels in the regions of lesions or optic disc. Source code is available at https://www.github.com/tyb311/SkelCon.

A Local and Global Feature Disentangled Network: Toward Classification of Benign-Malignant Thyroid Nodules From Ultrasound Image.

Thyroid nodules are one of the most common nodular lesions. The incidence of thyroid cancer has increased rapidly in the past three decades and is one of the cancers with the highest incidence. As a non-invasive imaging modality, ultrasonography can identify benign and malignant thyroid nodules, and it can be used for large-scale screening. In this study, inspired by the domain knowledge of sonographers when diagnosing ultrasound images, a local and global feature disentangled network (LoGo-Net) is proposed to classify benign and malignant thyroid nodules. This model imitates the dual-pathway structure of human vision and establishes a new feature extraction method to improve the recognition performance of nodules. We use the tissue-anatomy disentangled (TAD) block to connect the dual pathways, which decouples the cues of local and global features based on the self-attention mechanism. To verify the effectiveness of the model, we constructed a large-scale dataset and conducted extensive experiments. The results show that our method achieves an accuracy of 89.33%, which has the potential to be used in the clinical practice of doctors, including early cancer screening procedures in remote or resource-poor areas.

Retinal fundus image enhancement with image decomposition and visual adaptation.

Retinal fundus photography has been widely used to diagnose various prevalent diseases because many important diseases manifest themselves on the retina. However, the quality of fundus images obtained from practical clinical environments is not always good enough for diagnosis due to uneven illumination, blurring, low contrast, etc. In this paper, we propose a simple yet efficient method for fundus image enhancement. We first conduct image decomposition to divide the input image into three layers: base, detail, and noise layers; and then illumination correction, detail enhancement and denoising are conducted respectively at these three layers. Specifically, a simple visual adaptation model is used to correct the uneven illumination at the base layer and a weighted fusion is employed to enhance details and suppress noise and artifacts. The proposed method was evaluated on public datasets (DIARETDB0 and DIARETDB1), and also on some challenging images collected by us from the hospital. In addition, quality assessments by ophthalmologists were implemented to further verify the contribution of the proposed method in helping make diagnosis. Experimental results show that the proposed method outperforms other related methods and can simultaneously handle the tasks of illumination correction, detail enhancement and noise (and artifact) suppression.

A Biological Vision Inspired Framework for Image Enhancement in Poor Visibility Conditions.

Image enhancement is an important pre-processing step for many computer vision applications especially regarding the scenes in poor visibility conditions. In this work, we develop a unified two-pathway model inspired by the biological vision, especially the early visual mechanisms, which contributes to image enhancement tasks including low dynamic range (LDR) image enhancement and high dynamic range (HDR) image tone mapping. Firstly, the input image is separated and sent into two visual pathways: structure-pathway and detail-pathway, corresponding to the M-and P-pathway in the early visual system, which code the low-and high-frequency visual information, respectively. In the structure-pathway, an extended biological normalization model is used to integrate the global and local luminance adaptation, which can handle the visual scenes with varying illuminations. On the other hand, the detail enhancement and local noise suppression are achieved in the detail-pathway based on local energy weighting. Finally, the outputs of structure-and detail-pathway are integrated to achieve the low-light image enhancement. In addition, the proposed model can also be used for tone mapping of HDR images with some fine-tuning steps. Extensive experiments on three datasets (two LDR image datasets and one HDR scene dataset) show that the proposed model can handle the visual enhancement tasks mentioned above efficiently and outperform the related state-of-the-art methods.

Robot-Assisted Extraperitoneal Radical Prostatectomy, Single Site Plus Two Model.

PURPOSE: To demonstrate a feasible procedure of robot-assisted extraperitoneal radical prostatectomy single site plus two model to overcome the limitation of traditional single-port laparoscopic surgery. MATERIALS AND METHODS: All consecutive cases of robot-assisted extraperitoneal radical prostatectomy single site plus two model between November 2015 and April 2016 in our institution were included. We analyze the surgical and continence outcome. RESULTS: Twenty cases were included in the analysis. All cases successfully completed without any necessity for conversion to a standard laparoscopic approach or open surgery. The average age is 64.3 ± 8.2 years and average body mass index is 24.3 ± 2.9 kg/m2. Eight focal positive margins (40%) (5 in T2 and 3 in T3a disease) were encountered and all occurred at the apex. For continence outcomes, 9 (45%) patients need average 0-1 pads/day and 2 (10%) patients need average 3 pads/day after surgery, but most recover after several months. No intraoperative complications or major postoperative complications were recorded, excluding blood transfusion in one case. CONCLUSIONS: Robot-assisted extraperitoneal radical prostatectomy single site plus two model is technically feasible and safe in our experience. It can also be performed in patients that have previously received intraperitoneal abdominal surgery using the extraperitoneal approach. We can take this procedure into account for minimal invasive surgical option.

Character Decomposition and Transposition of Chinese Compound Words in the Right and Left Visual Fields.

This study investigated the character decomposition and transposition processes of Chinese two-character compound words (canonical and transposed words) and pseudowords in the right and left visual fields using a dual-target rapid serial visual presentation paradigm. The results confirmed a right visual field superiority for canonical words, but this advantage vanished for transposed words. The findings further indicated that the same quality of lexical processing could be obtained from the foveal and parafoveal regions of the right and left visual fields, regardless of the character order, but not in the periphery of the right visual field. Moreover, the proportion of order reversals peaked at the central position and the shortest exposure time, but it declined with increasing eccentricity and time interval. We concluded that the character transposition of Chinese compound words was significantly sensitive in the periphery of the right visual field. Furthermore, the character order errors were mainly encoded in the foveal vision with a duration of 100 ms, which suggested that the order of the foveally presented Chinese characters was more likely to be reversed at the early stage of visual word processing.

A Unified Framework for Salient Structure Detection by Contour-Guided Visual Search.

We define the task of salient structure (SS) detection to unify the saliency-related tasks, such as fixation prediction, salient object detection, and detection of other structures of interest in cluttered environments. To solve such SS detection tasks, a unified framework inspired by the two-pathway-based search strategy of biological vision is proposed in this paper. First, a contour-based spatial prior (CBSP) is extracted based on the layout of edges in the given scene along a fast non-selective pathway, which provides a rough, task-irrelevant, and robust estimation of the locations where the potential SSs are present. Second, another flow of local feature extraction is executed in parallel along the selective pathway. Finally, Bayesian inference is used to auto-weight and integrate the local cues guided by CBSP and to predict the exact locations of SSs. This model is invariant to the size and features of objects. The experimental results on six large datasets (three fixation prediction datasets and three salient object datasets) demonstrate that our system achieves competitive performance for SS detection (i.e., both the tasks of fixation prediction and salient object detection) compared with the state-of-the-art methods. In addition, our system also performs well for salient object construction from saliency maps and can be easily extended for salient edge detection.

Potential roles of the interaction between model V1 neurons with orientation-selective and non-selective surround inhibition in contour detection.

Both the neurons with orientation-selective and with non-selective surround inhibition have been observed in the primary visual cortex (V1) of primates and cats. Though the inhibition coming from the surround region (named as non-classical receptive field, nCRF) has been considered playing critical role in visual perception, the specific role of orientation-selective and non-selective inhibition in the task of contour detection is less known. To clarify above question, we first carried out computational analysis of the contour detection performance of V1 neurons with different types of surround inhibition, on the basis of which we then proposed two integrated models to evaluate their role in this specific perceptual task by combining the two types of surround inhibition with two different ways. The two models were evaluated with synthetic images and a set of challenging natural images, and the results show that both of the integrated models outperform the typical models with orientation-selective or non-selective inhibition alone. The findings of this study suggest that V1 neurons with different types of center-surround interaction work in cooperative and adaptive ways at least when extracting organized structures from cluttered natural scenes. This work is expected to inspire efficient phenomenological models for engineering applications in field of computational machine-vision.

Color Constancy Using Double-Opponency.

The double-opponent (DO) color-sensitive cells in the primary visual cortex (V1) of the human visual system (HVS) have long been recognized as the physiological basis of color constancy. In this work we propose a new color constancy model by imitating the functional properties of the HVS from the single-opponent (SO) cells in the retina to the DO cells in V1 and the possible neurons in the higher visual cortexes. The idea behind the proposed double-opponency based color constancy (DOCC) model originates from the substantial observation that the color distribution of the responses of DO cells to the color-biased images coincides well with the vector denoting the light source color. Then the illuminant color is easily estimated by pooling the responses of DO cells in separate channels in LMS space with the pooling mechanism of sum or max. Extensive evaluations on three commonly used datasets, including the test with the dataset dependent optimal parameters, as well as the intra- and inter-dataset cross validation, show that our physiologically inspired DOCC model can produce quite competitive results in comparison to the state-of-the-art approaches, but with a relative simple implementation and without requiring fine-tuning of the method for each different dataset.

Multifeature-based surround inhibition improves contour detection in natural images.

To effectively perform visual tasks like detecting contours, the visual system normally needs to integrate multiple visual features. Sufficient physiological studies have revealed that for a large number of neurons in the primary visual cortex (V1) of monkeys and cats, neuronal responses elicited by the stimuli placed within the classical receptive field (CRF) are substantially modulated, normally inhibited, when difference exists between the CRF and its surround, namely, non-CRF, for various local features. The exquisite sensitivity of V1 neurons to the center-surround stimulus configuration is thought to serve important perceptual functions, including contour detection. In this paper, we propose a biologically motivated model to improve the performance of perceptually salient contour detection. The main contribution is the multifeature-based center-surround framework, in which the surround inhibition weights of individual features, including orientation, luminance, and luminance contrast, are combined according to a scale-guided strategy, and the combined weights are then used to modulate the final surround inhibition of the neurons. The performance was compared with that of single-cue-based models and other existing methods (especially other biologically motivated ones). The results show that combining multiple cues can substantially improve the performance of contour detection compared with the models using single cue. In general, luminance and luminance contrast contribute much more than orientation to the specific task of contour extraction, at least in gray-scale natural images.

Clinical predictor of survival following docetaxel-based chemotherapy.

Prostate cancer (PCa) is the most common type of cancer in males in the USA and the incidence is increasing. For castration-resistant PCa (CRPC), previous studies have identified docetaxel-based chemotherapy as the first-line therapy. In the present study, the efficacy of docetaxel-based chemotherapy was investigated in a population of patients with CRPC. This study included 26 individuals (mean age, 73 years) with CRPC who were patients between July 2007 and October 2012 at the Kaohsiung Medical University Hospital (Kaohsiung, Taiwan). The regimen consisted of intravenous docetaxel (70 mg/m2) once every four weeks plus oral prednisolone (5 mg) twice daily for five days. Prostate-specific antigen (PSA) response (defined as a PSA decrease of >50% over four weeks), time to PSA progression, PCa-specific survival and overall survival (OS) were evaluated. For these 26 patients, the mean PSA level prior to chemotherapy treatment was 335.58 ng/ml. During follow-up, the average number of cycles of chemotherapy was approximately seven and 15 patients (58%) achieved a PSA response. PSA response was found to significantly correlate with OS and PCa-specific survival (P=0.014 and P=0.028, respectively). The mean value of the PSA nadir level was 89.97 ng/ml and time to PSA nadir was five months. The most common adverse event was leucopenia, which affected 88% of the patients. The results indicated that the length of time to PSA nadir and the occurrence of leucopenia may impact the PSA response. The docetaxel-based chemotherapy was a feasible and effective treatment regimen in patients with CRPC. However, the occurrence of adverse events, particularly the high incidence of leucopenia, may be cause for concern.