Unclear Boundaries Research Articles

Liver segmentation in abdominal CT images via auto-context neural network and self-supervised contour attention

Objective: Accurate image segmentation of the liver is a challenging problem owing to its large shape variability and unclear boundaries. Although the applications of fully convolutional neural networks (CNNs) have shown groundbreaking results, limited studies have focused on the performance of generalization. In this study, we introduce a CNN for liver segmentation on abdominal computed tomography (CT) images that focus on the performance of generalization and accuracy. Methods: To improve the generalization performance, we initially propose an auto-context algorithm in a single CNN. The proposed auto-context neural network exploits an effective high-level residual estimation to obtain the shape prior. Identical dual paths are effectively trained to represent mutual complementary features for an accurate posterior analysis of a liver. Further, we extend our network by employing a self-supervised contour scheme. We trained sparse contour features by penalizing the ground-truth contour to focus more contour attentions on the failures. Results: We used 180 abdominal CT images for training and validation. Two-fold cross-validation is presented for a comparison with the state-of-the-art neural networks. The experimental results show that the proposed network results in better accuracy when compared to the state-of-the-art networks by reducing 10.31% of the Hausdorff distance. Novel multiple N-fold cross-validations are conducted to show the best performance of generalization of the proposed network. Conclusion and significance: The proposed method minimized the error between training and test images more than any other modern neural networks. Moreover, the contour scheme was successfully employed in the network by introducing a self-supervising metric.

Deep learning-assisted microstructural analysis of Ni/YSZ anode composites for solid oxide fuel cells

Quantitative microstructural interpretations were carried out without human involvement through an integrated combination of deep learning and focused ion beam-scanning electron microscopy (FIB-SEM) analytics on Ni/Y2O3-stabilized ZrO2 (Ni/YSZ) cermets. The Ni/YSZ/pore composites were analyzed for the automated extraction of microstructural parameters to prevent the subjective analysis problems and unavoidable artifacts frequently encountered in lengthy image processing tasks and eliminate biased evaluations. Considering the high volume of image data and future expectations for electron microscopy usage, FIB-SEM was efficiently combined with semantic segmentation. Traditional image processing analysis tools are combined with phase separation predictions by semantic segmentation algorithms, leading to a quantitative evaluation of microstructural parameters. The combined strategy enables one to significantly enhance poor image quality originating from artifacts in electron microscopy, including charging effects, curtain effects, out-of-focus problems, and unclear phase boundaries encountered in searching for high-efficiency solid oxide fuel cells (SOFCs).

Bayesian Estimation of Geometric Morphometric Landmarks for Simultaneous Localization of Multiple Anatomies in Cardiac CT Images.

We propose a robust method to simultaneously localize multiple objects in cardiac computed tomography angiography (CTA) images. The relative prior distributions of the multiple objects in the three-dimensional (3D) space can be obtained through integrating the geometric morphological relationship of each target object to some reference objects. In cardiac CTA images, the cross-sections of ascending and descending aorta can play the role of the reference objects. We employed the maximum a posteriori (MAP) estimator that utilizes anatomic prior knowledge to address this problem of localizing multiple objects. We propose a new feature for each pixel using the relative distances, which can define any objects that have unclear boundaries. Our experimental results targeting four pulmonary veins (PVs) and the left atrial appendage (LAA) in cardiac CTA images demonstrate the robustness of the proposed method. The method could also be extended to localize other multiple objects in different applications.

OP-convNet: A Patch Classification-Based Framework for CT Vertebrae Segmentation

Accurate vertebrae segmentation from medical images plays an important role in clinical tasks of surgical planning, diagnosis, kyphosis, scoliosis, degenerative disc disease, spondylolisthesis, and post-operative assessment. Although the structures of bone have high contrast in medical images, vertebrae segmentation is a challenging task due to its complex structure, abnormal spine curves, and unclear boundaries. In recent years, deep learning has been widely applied in the segmentation of vertebrae images. In this paper, towards a robust and automatic segmentation system, we present an overlapping patch-based convNet (OP-convNet) model for automatic vertebrae CT images segmentation. Due to the greater memory and processing costs associated with 3D convolutional neural networks, as well as the risk of over-fitting, we employ overlapping patches in segmentation tasks using 2D convNet. In the proposed vertebrae segmentation method, OP-convNet effectively keeps the local information contained in CT images. We divide CT image slices into equal-sized square overlapping patches and applied the RUS-function on these patches for class balancing to minimize computational requirements. Then, these patches are input into the model along with their corresponding ground truth patches. This method has been evaluated on publicly available CT images from the MICCAI CSI workshop challenge. The results indicate that OP-convNet has precision (PRE) of 90.1%, specificity (SPE) of 99.4%, accuracy (ACC) of 98.8%, F-score of 90.1% in terms of the patch-based classification accuracy, and BF-score of 90.2%, sensitivity (SEN) of 90.3%, Jaccard index (JAC) of 82.3%, dice similarity score (DSC) of 89.9% in terms of the segmentation accuracy that outperform previous methods across all metrics.

Cross-Layer Feature Pyramid Network for Salient Object Detection.

Feature pyramid network (FPN) based models, which fuse the semantics and salient details in a progressive manner, have been proven highly effective in salient object detection. However, it is observed that these models often generate saliency maps with incomplete object structures or unclear object boundaries, due to the indirect information propagation among distant layers that makes such fusion structure less effective. In this work, we propose a novel Cross-layer Feature Pyramid Network (CFPN), in which direct cross-layer communication is enabled to improve the progressive fusion in salient object detection. Specifically, the proposed network first aggregates multi-scale features from different layers into feature maps that have access to both the high- and low- level information. Then, it distributes the aggregated features to all the involved layers to gain access to richer context. In this way, the distributed features per layer own both semantics and salient details from all other layers simultaneously, and suffer reduced loss of important information during the progressive feature fusion. At last, CFPN fuses the distributed features of each layer stage-by-stage. This way, the high-level features that contain context useful for locating complete objects are preserved until the final output layer, and the low-level features that contain spatial structure details are embedded into each layer to preserve spatial structural details. Extensive experimental results over six widely used salient object detection benchmarks and with three popular backbones clearly demonstrate that CFPN can accurately locate fairly complete salient regions and effectively segment the object boundaries.

Boundary Coding Representation for Organ Segmentation in Prostate Cancer Radiotherapy.

Accurate segmentation of the prostate and organs at risk (OARs, e.g., bladder and rectum) in male pelvic CT images is a critical step for prostate cancer radiotherapy. Unfortunately, the unclear organ boundary and large shape variation make the segmentation task very challenging. Previous studies usually used representations defined directly on unclear boundaries as context information to guide segmentation. Those boundary representations may not be so discriminative, resulting in limited performance improvement. To this end, we propose a novel boundary coding network (BCnet) to learn a discriminative representation for organ boundary and use it as the context information to guide the segmentation. Specifically, we design a two-stage learning strategy in the proposed BCnet: 1) Boundary coding representation learning. Two sub-networks under the supervision of the dilation and erosion masks transformed from the manually delineated organ mask are first separately trained to learn the spatial-semantic context near the organ boundary. Then we encode the organ boundary based on the predictions of these two sub-networks and design a multi-atlas based refinement strategy by transferring the knowledge from training data to inference. 2) Organ segmentation. The boundary coding representation as context information, in addition to the image patches, are used to train the final segmentation network. Experimental results on a large and diverse male pelvic CT dataset show that our method achieves superior performance compared with several state-of-the-art methods.

Implementation Of Technical Sub Committee Border Demarcation And Regulation (TSC-BDR) Agreement Between Indonesia - Timor Leste In The Resolution Of The Land Border Dispute

The unclear and strict land border between Indonesia and Timor Leste in some segments has caused a number of problems in bilateral relations between the two countries. Unclear demarcation (limitation of one country to another bordering on land) is one of the potential factors that can trigger conflicts between the citizens of the two countries living in the border region. The existence of repeated security disturbances at the borders of the two countries further reinforces the belief that in reality unclear land boundaries between Indonesia and Timor Leste, it can easily erupt in disputes, disputes and conflicts, both between communities, or between communities and security forces. The aim of this study is to analyze the Implementation of Technical Sub Committee Border Demarcation and Regulation (TSC-BDR) Between Indonesia - Timor Leste in Completion of The Border Dispute Disorders. To answer the legal issue this study using normative legal research. Result of this study is the Border Demarcation and Regulation (TSC - BDR) Technical Sub Committee forum was established independently under the coordination of JBC Indonesia - Timor Leste which specifically discussed the technical aspects of the Indonesia - Timor Leste border. It is hoped that the formation of the TSC-BDR can help Indonesia and Timor Leste solve the land border problems of the two countries.

MR Brain Image Segmentation Optimized by Using Ant Colony Algorithm with BrainSeg3D for Multiple Sclerosis Tumors Detection

Nowadays, Brain tumors segmentation is a very important task in diagnosis of brain lesions.There are diversapproaches of segmentation, but segmentation of brain tumors is always difficult due to the complex features of magnetic resonance imaging MRI such as the appearance of vague and unclear tumors and boundaries. In this paper, an automatic MRI segmentation method is used to solve these problems. Here, tumor segmentation is treated as a problem of classification using the Ant Colony ACO optimization algorithm combined with a proposed protocol based on BrainSeg3D tools. Many studies and many existing approaches tend the multiple sclerosis (MS) which is a chronic inflammatory anomaly of the central nervous system. To pick up the outliers of multiple sclerosis, we should do a diagnosis by magnetic resonance imaging with various sequences. For this aim, some late technics of segmentation and classification of MRI images have been suggested for the automatic detection of MS outliers. In this work, we submit a novel protocol based on current and novel semi-automated tumors segmentation technics of BrainSeg3D. Evaluation of our results was performed on novel MR database containing 30 MS patients, which were learned with a 3T MR scanner with conventional sequences so that to estimate our assessment with the acquired consensus segmentation with ground truth data. The submitted algorithm is evaluated by using MATLAB GUI program and BrainSeg3D tools.

Fundus imaging features of purified protein derivative and T-spot positive tubercular serpiginous-like choroiditis

Objective: To summarize the clinical and fundus imaging features of purified protein derivative and T-spot positive tubercular serpiginous-like choroiditis (PTP-SLC) patients. Methods: This retrospective study consecutively enrolled 13 PTP-SLC patients (21 eyes) in Beijing Tongren Hospital from November 2015 to November 2017. There were 8 males and 5 females with an average age of (45.2±12.1) years. Medical history and results of systemic and ophthalmological examinations, such us fundus autofluorescence photography, optical coherence tomography (OCT), fluorescein fundus angiography (FFA) and indocyanine green angiography, were evaluated. Results: Eight patients had binocular disease with an average interval time of (8.4±7.9) years. The average visual acuity of all patients was 0.3, and 4 patients had a clear history of exposure to tuberculosis. The active lesions in the PTP-SLC patients were homogeneous and creamish-yellow with unclear boundaries. Fundus autofluorescence showed an ill-defined, diffuse hyperautofluorescent zone. OCT showed punctate hyperreflexes between the choroidal stroma, destruction of the outer retinal structure with intraretinal edema and discrete vitreal hyper-reflective spots. FFA showed hypofluorescence in the active lesion at early stage and diffuse hyperfluorescence with leakage. Indocyanine green angiography showed persistent hypofluorescence. Conclusions: PTP-SLC fundus lesions are mainly manifested as homogeneous creamish-yellow lesions with unclear boundaries and high in autofluorescence. The involvement of the choroid and the outer layer of the retina can be observed on OCT. FFA can find more retinal vascular inflammatory changes. It is difficult to distinguish PTP-SLC from serpiginous choroiditis simply based on clinical and epidemiological characteristics. The pathogenic examination of tuberculosis is still the key to differential diagnosis (Chin J Ophthalmol, 2020, 56: 914-919).

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><strong><em>Pertya ferruginea</em> (Asteraceae: Pertyeae), a new species from southeastern China, with a revision of <em>P. cordifolia</em> and <em>P. pubescens</em></strong></p></div></div></div>

Pertya ferruginea Cai F. Zhang, a new species of the genus Pertya (Asteraceae: Pertyeae) from southeastern China, is described and illustrated with a revision of P. cordifolia and P. pubescens. Specimens of the new species had been usually mis-identified as the latter two species because of unclear boundaries among most known species of the genus in southeastern China. Based on extensive field collections and much more herbarium specimens examined than previous studies, P. cordifolia and P. pubescens are revised with much narrower circumscriptions. Although very similar to P. pubescens, the new species differs in the indumentum, length of involucre, number of series of involucral bracts and corolla size. Conservation status of the new species and previous misidentifications relevant to the three species are briefly discussed.

Diagnosis and treatment of tumors involving the root of neck: experience of 73 cases

Objective:To summarize and analyze the clinical diagnosis and surgical treatment of patients with tumors involving the root of neck. Method:We retrospectively analyzed the clinical data of 73 patients with tumors involving the root of neck in Peking University First Hospital Otorhinolaryngology Head and Neck Surgery department. Data collected included clinical manifestations, preoperative imaging evaluation, surgical approach selection, postoperative pathological types, postoperative complications and prognosis. Result:The most frequent symptom was a painless cervical mass（36 cases） and dysphagia（16 cases）. All patients underwent preoperative enhanced CT scan or MRI, which would be helpful to evaluate the tumor size, shape, location, relationship with surrounding structures, especially important blood vessels, and secondary changes. The postoperative pathological diagnosis included 37 cases of benign and 36 cases of malignant. The most common benign tumor was retrosternal goiter in 17 cases, and the most common malignant tumor was cervical esophageal cancer in 15 cases. Nonsurgical treatment was performed in 3 cases ,while surgical treatment was performed in 70 cases, including 61 cases （87.1%） with cervical approach , 9 cases （12.9%） with combined cervicothoracic approach , 67 cases of complete tumor resection and 3 cases of palliative resection. Sixteen cases cooperated with thoracic surgerons, and 1 case with orthopedic surgerons. Surgical complications occurred in 16 cases （22.9%）. Seven patients were lost to follow-up, and 66 patients were followed up for 3 months to 15 years. None of the 35 patients with benign tumors had recurrence, and among the 31 patients with malignant tumors, the 3-year survival rate was 48.4% and the 5-year survival rate was 32.3%. Conclusion:Tumors involving the root of neck are challenging to diagnose and treat due to the complex regional anatomy and a variety of pathological types, with comparable proportion of benign and malignant tumors. Surgery is the first choice, but it requires careful preoperative assessment. Surgical approaches include cervical approach and combined cervicothoracic approach, which should be determined according to the pathology, size and surrounding structure of the tumor, as well as the habits of the surgeon. Most benign tumors can be excised by the cervical approach. The combined cervicothoracic approach is suitable for malignant tumors with unclear boundaries and close adhesion of important blood vessels and nerves. Proper treatment of large vessels is the key to complete resection of tumors. There are many complications in the operation of this site, so it is necessary to fully communicate with the patient before operation, and sometimes multidisciplinary cooperation is needed.

Toward real-time polyp detection using fully CNNs for 2D Gaussian shapes prediction.

To decrease colon polyp miss-rate during colonoscopy, a real-time detection system with high accuracy is needed. Recently, there have been many efforts to develop models for real-time polyp detection, but work is still required to develop real-time detection algorithms with reliable results. We use single-shot feed-forward fully convolutional neural networks (F-CNN) to develop an accurate real-time polyp detection system. F-CNNs are usually trained on binary masks for object segmentation. We propose the use of 2D Gaussian masks instead of binary masks to enable these models to detect different types of polyps more effectively and efficiently and reduce the number of false positives. The experimental results showed that the proposed 2D Gaussian masks are efficient for detection of flat and small polyps with unclear boundaries between background and polyp parts. The masks make a better training effect to discriminate polyps from the polyp-like false positives. The proposed method achieved state-of-the-art results on two polyp datasets. On the ETIS-LARIB dataset we achieved 86.54% recall, 86.12% precision, and 86.33% F1-score, and on the CVC-ColonDB we achieved 91% recall, 88.35% precision, and F1-score 89.65%.

Video Summarization Based on Mutual Information and Entropy Sliding Window Method.

This paper proposes a video summarization algorithm called the Mutual Information and Entropy based adaptive Sliding Window (MIESW) method, which is specifically for the static summary of gesture videos. Considering that gesture videos usually have uncertain transition postures and unclear movement boundaries or inexplicable frames, we propose a three-step method where the first step involves browsing a video, the second step applies the MIESW method to select candidate key frames, and the third step removes most redundant key frames. In detail, the first step is to convert the video into a sequence of frames and adjust the size of the frames. In the second step, a key frame extraction algorithm named MIESW is executed. The inter-frame mutual information value is used as a metric to adaptively adjust the size of the sliding window to group similar content of the video. Then, based on the entropy value of the frame and the average mutual information value of the frame group, the threshold method is applied to optimize the grouping, and the key frames are extracted. In the third step, speeded up robust features (SURF) analysis is performed to eliminate redundant frames in these candidate key frames. The calculation of Precision, Recall, and F are optimized from the perspective of practicality and feasibility. Experiments demonstrate that key frames extracted using our method provide high-quality video summaries and basically cover the main content of the gesture video.

Automatic detection and segmentation of lumbar vertebrae from X-ray images for compression fracture evaluation

For compression fracture detection and evaluation, an automatic X-ray image segmentation technique that combines deep-learning and level-set methods is proposed. Automatic segmentation is much more difficult for X-ray images than for CT or MRI images because they contain overlapping shadows of thoracoabdominal structures including lungs, bowel gases, and other bony structures such as ribs. Additional difficulties include unclear object boundaries, the complex shape of the vertebra, inter-patient variability, and variations in image contrast. Accordingly, a structured hierarchical segmentation method is presented that combines the advantages of two deep-learning methods. Pose-driven learning is used to selectively identify the five lumbar vertebrae in an accurate and robust manner. With knowledge of the vertebral positions, M-net is employed to segment the individual vertebra. Finally, fine-tuning segmentation is applied by combining the level-set method with the previously obtained segmentation results. The performance of the proposed method was validated by 160 lumbar X-ray images, resulting in a mean Dice similarity metric of 91.60±2.22%. The results show that the proposed method achieves accurate and robust identification of each lumbar vertebra and fine segmentation of individual vertebra.

The Role of Contrast-Enhanced Ultrasound in Evaluating Gestational Trophoblastic Neoplasia: A Preliminary Study.

ObjectiveWe aimed to compare the imaging features of gestational trophoblastic neoplasias (GTNs) and benign pregnancy-related uterine diseases on contrast-enhanced ultrasound (CEUS) to explore the clinical value of CEUS in differentiating these two types of uterine lesions.Materials and MethodsIn this prospective study, patients with GTNs before and after chemotherapy and benign pregnancy-related uterine lesions received conventional US and CEUS. The imaging features and parameters of CEUS of the lesions were assessed.ResultsA total of 73 patients were enrolled in this study, including 48 with GTNs and 25 with benign lesions. The enhancement pattern of newly diagnosed GTNs was mainly diffuse enhancement (70.8%), with a non-enhanced area of less than 1/3 of the entire lesion. For the benign group, the enhancement pattern was mainly ring-shaped enhancement (72.0%), and the non-enhanced area was more than 1/2 (68.0%), significantly different from that of GTNs (P = 0.000 and 0.002, respectively). Forty-one lesions demonstrated unclear boundaries on greyscale US but clearly displayed on CEUS. In both the benign and malignant groups, the measurements of lesion size on CEUS were larger than those on greyscale US (P = 0.000). The measurement differences (ΔD: D2-D1) of the malignant cases were higher than those of the benign cases (P = 0.001).ConclusionGTNs and benign pregnancy-related uterine diseases have different imaging characteristics on CEUS. The boundary and involved range of the lesion can be clearly demonstrated on CEUS. CEUS possesses clinical value in diagnosing and evaluating GTNs and benign pregnancy-related diseases.

A hybrid interval type-2 semi-supervised possibilistic fuzzy c-means clustering and particle swarm optimization for satellite image analysis

Although satellite images can provide more information about the earth’s surface in a relatively short time and over a large scale, they are affected by observation conditions and the accuracy of the image acquisition equipment. The objects on the images are often not clear and uncertain, especially at their borders. The type-1 fuzzy set based fuzzy clustering technique allows each data pattern to belong to many different clusters through membership function (MF) values, which can handle data patterns with unclear and uncertain boundaries well. However, this technique is quite sensitive to noise, outliers, and limitations in handling uncertainties. To overcome these disadvantages, we propose a hybrid method encompassing interval type-2 semi-supervised possibilistic fuzzy c-means clustering (IT2SPFCM) and Particle Swarm Optimization (PSO) to form the proposed IT2SPFCM-PSO. We experimented on some satellite images to prove the effectiveness of the proposed method. Experimental results show that the IT2SPFCM-PSO algorithm gives accuracy from 98.8% to 99.39% and is higher than that of other matching algorithms including SFCM, SMKFCM, SIIT2FCM, PFCM, SPFCM-W, SPFCM-SS, and IT2SPFCM. Analysis of the results by indicators PC-I, CE-I, D-I, XB-I, t -I, and MSE also showed that the proposed method gives better results in most experiments.

Molecular and phenotypic data reveal a new Amazonian species of pit vipers (Serpentes: Viperidae: Bothrops)

ABSTRACT Recent genetic studies have found unclear species boundaries and evidence of undescribed diversity in the poorly studied jararacussu species group within Bothrops. In this contribution, we investigate phenotypic and genetic diversity in the Amazonian snake Bothrops brazili to test previous assertions of unrecognised species diversity within this taxon. Our phylogenetic results and inferences of independently evolving lineages based on molecular data recover two divergent clades within B. brazili, one restricted to areas north and another to areas south of the Amazon River. Phylogenetic relationships between these lineages and other species in the jararacussu species group reveal B. brazili to be paraphyletic, with the northern clade inferred as sister to a clade composed of Atlantic Forest taxa (B. jararacussu, B. muriciensis, B. pirajai). External morphology (number of ventral and subcaudal scales) and colouration patterns (lateral trapezoidal marks) consistently separate the two lineages of B. brazili. We therefore recognise and describe the northern lineage as a new species of Bothrops, improving our knowledge of species diversity within a medically important clade of venomous South American snakes.

A novel LRRFIP1-ALK fusion in inflammatory myofibroblastic tumor of hip and response to crizotinib.

An inflammatory myofibroblastic tumor (IMT) is a rare invasive soft tissue mass with intramuscular penetration that is primarily treated via a surgical procedure. However, with unclear boundaries and a high rate of relapse, there is no standard treatment for recurrence or unresectable tumors. It is noteworthy that approximately half of IMTs harbor genetic rearrangements of the anaplastic lymphoma kinase (ALK). ALK inhibitors have been used successfully in the treatment of IMTs with a variety of ALK fusions. Here, we present a case of a 15-year-old patient with IMT around the hip. Next-generation sequencing (NGS) revealed an LRRFIP1-ALK fusion, which has not yet been reported in the literature. Crizotinib, an ALK inhibitor, was effective in the treatment of this patient, indicating that ALK inhibitors may be effective for IMT with LRRFIP1-ALK fusions. This report expands the list of gene fusions in IMTs and highlights a new target for treatment.

Overfeeding-Induced Obesity Could Cause Potential Immuno-Physiological Disorders in Rainbow Trout (Oncorhynchus mykiss).

Simple SummaryIn this study, we have successfully generated overfeeding-induced obesity in rainbow trout and demonstrated that the overfeeding regime causes adverse effects on the health status of fish. What we found in this study is that fish in the overfed group harbor enlarged liver and macrophages due to high lipid accumulation. The expression of IL-10, CD36, TLR2, and HSP70 (stress and/or obesity-related genes) was significantly upregulated in overfed fish. Moreover, oxidized low-density lipoprotein (OxLDL), which is known to be produced more in obese individuals, caused apoptosis of trout lymphocyte. These results clearly indicate that overfeeding-induced obesity can be the source of stress and cause immuno-physiological disorders in rainbow trout. Awareness and knowledge of farmers on the relationship between overfeeding and fish health might contribute to increased disease resistance and aquaculture production.Although over-nutrition from overfeeding-induced obesity is known to be highly associated with metabolic and immunological disorders in humans, little is known about overfeeding-induced obesity in fish farming. The purpose of this study was to investigate changes in immuno-physiological parameters, to better understand the potential risk of overfeeding–induced obesity in fish. Commercial feed was provided to fish in the overfed group until they refuse to eat, but fish in the control group was fed with the feed at 1% bodyweight per day. The hemato-serological, histological, and immunological changes were observed at weeks 2 and 8. Rainbow trout leukocytes were co-incubated with oxidized low-density lipoprotein (OxLDL), and the phagocytes engulfing the OxLDL and the presence of apoptotic cells were evaluated. The body weight, body mass index (BMI), and hepatosomatic index (HSI) index were significantly higher in the overfed group, and high lipid accumulation and fatty changes were also observed in their livers, indicating that the feeding regime used in this study led to overfeeding-induced obesity. Likewise, much higher numbers of and larger vacuoles were observed in overfed fish macrophages, showing unclear boundaries between the cytoplasm and extracellular space. In the overfed group, the expression of IL-10, HSP70, TLR2, and CD36 was significantly higher, and lymphocyte apoptosis was more evident, indicating that overfeeding-induced obese fish might have immunologic disorders. This was the first study to demonstrate that overfeeding-induced obesity could cause an immune-physiological imbalance in rainbow trout, making them more vulnerable to infectious diseases and various stressful conditions. This study will contribute to improvements in fish nutrition, feeding practices, fish nutrition, and disease prevention in the aquaculture industry.

Multiple Optical Sensor Fusion for Mineral Mapping of Core Samples.

Geological objects are characterized by a high complexity inherent to a strong compositional variability at all scales and usually unclear class boundaries. Therefore, dedicated processing schemes are required for the analysis of such data for mineralogical mapping. On the other hand, the variety of optical sensing technology reveals different data attributes and therefore multi-sensor approaches are adapted to solve such complicated mapping problems. In this paper, we devise an adapted multi-optical sensor fusion (MOSFus) workflow which takes the geological characteristics into account. The proposed processing chain exhaustively covers all relevant stages, including data acquisition, preprocessing, feature fusion, and mineralogical mapping. The concept includes (i) a spatial feature extraction based on morphological profiles on RGB data with high spatial resolution, (ii) a specific noise reduction applied on the hyperspectral data that assumes mixed sparse and Gaussian contamination, and (iii) a subsequent dimensionality reduction using a sparse and smooth low rank analysis. The feature extraction approach allows one to fuse heterogeneous data at variable resolutions, scales, and spectral ranges and improve classification substantially. The last step of the approach, an SVM classifier, is robust to unbalanced and sparse training sets and is particularly efficient with complex imaging data. We evaluate the performance of the procedure with two different multi-optical sensor datasets. The results demonstrate the superiority of this dedicated approach over common strategies.