Delaunay triangulation-based pit density estimation for the classification of polyps in high-magnification chromo-colonoscopy

The global biogeography and environmental drivers of fairy circles

Fairy circles (FCs) are regular vegetation patterns found in drylands of Namibia and Western Australia. It is virtually unknown whether they are also present in other regions of the world and which environmental factors determine their distribution. We conducted a global systematic survey and found FC-like vegetation patterns in 263 sites from 15 countries and three continents, including the Sahel, Madagascar, and Middle-West Asia. FC-like vegetation patterns are found in environments characterized by a unique combination of soil (including low nutrient levels and high sand content) and climatic (arid regions with high temperatures and high precipitation seasonality) conditions. In addition to these factors, the presence of specific biological elements (termite nests) in certain regions also plays a role in the presence of these patterns. Furthermore, areas with FC-like vegetation patterns also showed more stable temporal productivity patterns than those of surrounding areas. Our study presents a global atlas of FCs and provides unique insights into the ecology and biogeography of these fascinating vegetation patterns.

Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review

Magnetic resonance imaging (MRI) is a standard tool for the diagnosis of stroke, but its manual interpretation by experts is arduous and time-consuming. Thus, there is a need for computer-aided-diagnosis (CAD) models for the automatic segmentation and classification of stroke on brain MRI. The heterogeneity of stroke pathogenesis, morphology, image acquisition modalities, sequences, and intralesional tissue signal intensity, as well as lesion-to-normal tissue contrast, pose significant challenges to the development of such systems. Machine learning (ML) is increasingly being used in predictive neuroimaging diagnosis and prognostication. This paper reviews image processing and machine learning techniques that have been applied to detect ischemic stroke on brain MRI, including details on image acquisition, pre-processing, techniques to segment, extraction of features, and classification into stroke types. The main objective of this work is to find the state-of-art machine learning techniques used to predict the ischemic stroke and their application in clinical set-up. The article selection is performed according to PRISMA guideline. The state-of-the-art on automated MRI stroke diagnosis, with a focus on machine learning, is discussed, along with its advantages and limitations. We found that the various machine learning models discussed in this article are able to detect the infarcts with an acceptable accuracy of 70–90%. However, no one has highlighted the time complexity to predict the stroke in the model developed, which is an important factor. The work concludes with proposals for future recommendations for building efficient and robust deep learning (DL) models for quantitative brain MRI analysis. In recent work, with the application of DL approaches, using large datasets to train the models has improved the detection accuracy and reduced computational complexity. We suggest that the design of a decision support system based on artificial intelligence (AI) and clinical data presenting symptoms is essential to support clinicians to accelerate diagnosis and timeous therapy in the emergency management of stroke.

Current Status and Future Perspectives of Artificial Intelligence in Colonoscopy

The early endoscopic identification, resection, and treatment of precancerous adenoma and early-stage cancer has been shown to reduce not only the prevalence of colorectal cancer but also its mortality rate. Recent advances in endoscopic devices and imaging technology have dramatically improved our ability to detect colorectal lesions and predict their pathological diagnosis. In addition to this, rapid advances in artificial intelligence (AI) technology mean that AI-related research and development is now progressing in the diagnostic imaging field, particularly colonoscopy, and AIs (i.e., devices that mimic cognitive abilities, such as learning and problem-solving) already approved as medical devices are now being introduced into everyday clinical practice. Today, there is an increasing expectation that sophisticated AIs will be able to provide high-level diagnostic performance irrespective of the level of skill of the endoscopist. In this paper, we review colonoscopy-related AI research and the AIs that have already been approved and discuss the future prospects of this technology.

Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient's Stratification

Colorectal cancer (CRC) is the second most frequently diagnosed type of cancer and a major worldwide public health concern. Despite the global efforts in the development of modern therapeutic strategies, CRC prognosis is strongly correlated with the stage of the disease at diagnosis. Early detection of CRC has a huge impact in decreasing mortality while pre-lesion detection significantly reduces the incidence of the pathology. Even though the management of CRC patients is based on robust diagnostic methods such as serum tumor markers analysis, colonoscopy, histopathological analysis of tumor tissue, and imaging methods (computer tomography or magnetic resonance), these strategies still have many limitations and do not fully satisfy clinical needs due to their lack of sensitivity and/or specificity. Therefore, improvements of the current practice would substantially impact the management of CRC patients. In this view, liquid biopsy is a promising approach that could help clinicians screen for disease, stratify patients to the best treatment, and monitor treatment response and resistance mechanisms in the tumor in a regular and minimally invasive manner. Liquid biopsies allow the detection and analysis of different tumor-derived circulating markers such as cell-free nucleic acids (cfNA), circulating tumor cells (CTCs), and extracellular vesicles (EVs) in the bloodstream. The major advantage of this approach is its ability to trace and monitor the molecular profile of the patient's tumor and to predict personalized treatment in real-time. On the other hand, the prospective use of artificial intelligence (AI) in medicine holds great promise in oncology, for the diagnosis, treatment, and prognosis prediction of disease. AI has two main branches in the medical field: (i) a virtual branch that includes medical imaging, clinical assisted diagnosis, and treatment, as well as drug research, and (ii) a physical branch that includes surgical robots. This review summarizes findings relevant to liquid biopsy and AI in CRC for better management and stratification of CRC patients.

MORPHOLOGICAL CHARACTERIZATION OF COLORECTAL PITS USING AUTOFLUORESCENCE MICROSCOPY IMAGES

BACKGROUND

Colorectal cancer is one of the most prevalent pathologies. Its prognosis is linked to the early detection and treatment. Currently diagnosis is performed by histological analysis from polyp biopsies, followed by morphological classification. Kudo's pit pattern classification is frequently used for the differentiation of neoplastic colorectal lesions using hematoxylin-eosin stained samples. Few articles have reported this classification with image software processing, using exogenous markers over the samples. The processing of autofluorescence images is an alternative that could allow the characterization of the pits from the crypts of Lieberkühn, bypassing staining techniques.

OBJECTIVE

Processing and analysis of widefield autofluorescence microscopy images obtained by fresh colon tissue samples from a murine model of colorectal cancer in order to quantify and characterize the pits morphology by measuring morphology parameters and shape descriptors.

METHODS

Adult male BALB/cCmedc strain mice (n=27), ranging from 20 to 30 g, were randomly assigned to four and five groups of treated and control animals. Colon samples were collected at day zero and at fourth, eighth, sixteenth and twentieth weeks after treatmentwith azoxymethane. Two-dimensional (2D) segmentation, quantification and morphological characterization of pits by image processing applied using macro programming from FIJI.

RESULTS

Type I is the pit morphology prevailing between 53 and 81% in control group weeks. III-L and III-S types were detected in reduced percentages. Between the 33 and 56% of type I was stated as the prevailing morphology for the 4th, 8th and 20th weeks of treated groups, followed by III-L type. For the 16th week, the 39% of the pits was characterized as III-L type, followed by type I. Further, pattern types as IV, III-S and II were also found mainly in that order for almost all of the treated weeks.

CONCLUSION

These preliminaries outcomes could be considered an advance in two-dimensional pit characterization as the whole image processing, comparing to the conventional procedure, takes a few seconds to quantify and characterize non-pathological colon pits as well as to estimate early pathological stages of colorectal cancer.

Quest for the best endoscopic imaging modality for computer-assisted colonic polyp staging

BACKGROUND

It was shown in previous studies that high definition endoscopy, high magnification endoscopy and image enhancement technologies, such as chromoendoscopy and digital chromoendoscopy [narrow-band imaging (NBI), i-Scan] facilitate the detection and classification of colonic polyps during endoscopic sessions. However, there are no comprehensive studies so far that analyze which endoscopic imaging modalities facilitate the automated classification of colonic polyps. In this work, we investigate the impact of endoscopic imaging modalities on the results of computer-assisted diagnosis systems for colonic polyp staging.

AIM

To assess which endoscopic imaging modalities are best suited for the computer-assisted staging of colonic polyps.

METHODS

In our experiments, we apply twelve state-of-the-art feature extraction methods for the classification of colonic polyps to five endoscopic image databases of colonic lesions. For this purpose, we employ a specifically designed experimental setup to avoid biases in the outcomes caused by differing numbers of images per image database. The image databases were obtained using different imaging modalities. Two databases were obtained by high-definition endoscopy in combination with i-Scan technology (one with chromoendoscopy and one without chromoendoscopy). Three databases were obtained by high-magnification endoscopy (two databases using narrow band imaging and one using chromoendoscopy). The lesions are categorized into non-neoplastic and neoplastic according to the histological diagnosis.

RESULTS

Generally, it is feature-dependent which imaging modalities achieve high results and which do not. For the high-definition image databases, we achieved overall classification rates of up to 79.2% with chromoendoscopy and 88.9% without chromoendoscopy. In the case of the database obtained by high-magnification chromoendoscopy, the classification rates were up to 81.4%. For the combination of high-magnification endoscopy with NBI, results of up to 97.4% for one database and up to 84% for the other were achieved. Non-neoplastic lesions were classified more accurately in general than non-neoplastic lesions. It was shown that the image recording conditions highly affect the performance of automated diagnosis systems and partly contribute to a stronger effect on the staging results than the used imaging modality.

CONCLUSION

Chromoendoscopy has a negative impact on the results of the methods. NBI is better suited than chromoendoscopy. High-definition and high-magnification endoscopy are equally suited.

Training of polyp staging systems using mixed imaging modalities

BACKGROUND

In medical image data sets, the number of images is usually quite small. The small number of training samples does not allow to properly train classifiers which leads to massive overfitting to the training data. In this work, we investigate whether increasing the number of training samples by merging datasets from different imaging modalities can be effectively applied to improve predictive performance. Further, we investigate if the extracted features from the employed image representations differ between different imaging modalities and if domain adaption helps to overcome these differences.

METHOD

We employ twelve feature extraction methods to differentiate between non-neoplastic and neoplastic lesions. Experiments are performed using four different classifier training strategies, each with a different combination of training data. The specifically designed setup for these experiments enables a fair comparison between the four training strategies.

RESULTS

Combining high definition with high magnification training data and chromoscopic with non-chromoscopic training data partly improved the results. The usage of domain adaptation has only a small effect on the results compared to just using non-adapted training data.

CONCLUSION

Merging datasets from different imaging modalities turned out to be partially beneficial for the case of combining high definition endoscopic data with high magnification endoscopic data and for combining chromoscopic with non-chromoscopic data. NBI and chromoendoscopy on the other hand are mostly too different with respect to the extracted features to combine images of these two modalities for classifier training.

Fisher encoding of convolutional neural network features for endoscopic image classification

We propose an approach for the automated diagnosis of celiac disease (CD) and colonic polyps (CP) based on applying Fisher encoding to the activations of convolutional layers. In our experiments, three different convolutional neural network (CNN) architectures (AlexNet, VGG-f, and VGG-16) are applied to three endoscopic image databases (one CD database and two CP databases). For each network architecture, we perform experiments using a version of the net that is pretrained on the ImageNet database, as well as a version of the net that is trained on a specific endoscopic image database. The Fisher representations of convolutional layer activations are classified using support vector machines. Additionally, experiments are performed by concatenating the Fisher representations of several layers to combine the information of these layers. We will show that our proposed CNN-Fisher approach clearly outperforms other CNN- and non-CNN-based approaches and that our approach requires no training on the target dataset, which results in substantial time savings compared with other CNN-based approaches.

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification

Recently, Deep Learning, especially through Convolutional Neural Networks (CNNs) has been widely used to enable the extraction of highly representative features. This is done among the network layers by filtering, selecting, and using these features in the last fully connected layers for pattern classification. However, CNN training for automated endoscopic image classification still provides a challenge due to the lack of large and publicly available annotated databases. In this work we explore Deep Learning for the automated classification of colonic polyps using different configurations for training CNNs from scratch (or full training) and distinct architectures of pretrained CNNs tested on 8-HD-endoscopic image databases acquired using different modalities. We compare our results with some commonly used features for colonic polyp classification and the good results suggest that features learned by CNNs trained from scratch and the “off-the-shelf” CNNs features can be highly relevant for automated classification of colonic polyps. Moreover, we also show that the combination of classical features and “off-the-shelf” CNNs features can be a good approach to further improve the results.

Hybrid detection of lung nodules on CT scan images

PURPOSE

The diversity of lung nodules poses difficulty for the current computer-aided diagnostic (CAD) schemes for lung nodule detection on computed tomography (CT) scan images, especially in large-scale CT screening studies. We proposed a novel CAD scheme based on a hybrid method to address the challenges of detection in diverse lung nodules.

METHODS

The hybrid method proposed in this paper integrates several existing and widely used algorithms in the field of nodule detection, including morphological operation, dot-enhancement based on Hessian matrix, fuzzy connectedness segmentation, local density maximum algorithm, geodesic distance map, and regression tree classification. All of the adopted algorithms were organized into tree structures with multi-nodes. Each node in the tree structure aimed to deal with one type of lung nodule.

RESULTS

The method has been evaluated on 294 CT scans from the Lung Image Database Consortium (LIDC) dataset. The CT scans were randomly divided into two independent subsets: a training set (196 scans) and a test set (98 scans). In total, the 294 CT scans contained 631 lung nodules, which were annotated by at least two radiologists participating in the LIDC project. The sensitivity and false positive per scan for the training set were 87% and 2.61%. The sensitivity and false positive per scan for the testing set were 85.2% and 3.13%.

CONCLUSIONS

The proposed hybrid method yielded high performance on the evaluation dataset and exhibits advantages over existing CAD schemes. We believe that the present method would be useful for a wide variety of CT imaging protocols used in both routine diagnosis and screening studies.

Making texture descriptors invariant to blur

Besides a high distinctiveness, robustness (or invariance) to image degradations is very desirable for texture feature extraction methods in real-world applications. In this paper, focus is on making arbitrary texture descriptors invariant to blur which is often prevalent in real image data. From previous work, we know that most state-of-the-art texture feature extraction methods are unable to cope even with minor blur degradations if the classifier's training stage is based on idealistic data. However, if the training set suffers similarly from the degradations, the obtained accuracies are significantly higher. Exploiting that knowledge, in this approach the level of blur of each image is increased to a certain threshold, based on the estimation of a blur measure. Experiments with synthetically degraded data show that the method is able to generate a high degree of blur invariance without loosing too much distinctiveness. Finally, we show that our method is not limited to ideal Gaussian blur.

Directional wavelet based features for colonic polyp classification

In this work, various wavelet based methods like the discrete wavelet transform, the dual-tree complex wavelet transform, the Gabor wavelet transform, curvelets, contourlets and shearlets are applied for the automated classification of colonic polyps. The methods are tested on 8 HD-endoscopic image databases, where each database is acquired using different imaging modalities (Pentax's i-Scan technology combined with or without staining the mucosa), 2 NBI high-magnification databases and one database with chromoscopy high-magnification images. To evaluate the suitability of the wavelet based methods with respect to the classification of colonic polyps, the classification performances of 3 wavelet transforms and the more recent curvelets, contourlets and shearlets are compared using a common framework. Wavelet transforms were already often and successfully applied to the classification of colonic polyps, whereas curvelets, contourlets and shearlets have not been used for this purpose so far. We apply different feature extraction techniques to extract the information of the subbands of the wavelet based methods. Most of the in total 25 approaches were already published in different texture classification contexts. Thus, the aim is also to assess and compare their classification performance using a common framework. Three of the 25 approaches are novel. These three approaches extract Weibull features from the subbands of curvelets, contourlets and shearlets. Additionally, 5 state-of-the-art non wavelet based methods are applied to our databases so that we can compare their results with those of the wavelet based methods. It turned out that extracting Weibull distribution parameters from the subband coefficients generally leads to high classification results, especially for the dual-tree complex wavelet transform, the Gabor wavelet transform and the Shearlet transform. These three wavelet based transforms in combination with Weibull features even outperform the state-of-the-art methods on most of the databases. We will also show that the Weibull distribution is better suited to model the subband coefficient distribution than other commonly used probability distributions like the Gaussian distribution and the generalized Gaussian distribution. So this work gives a reasonable summary of wavelet based methods for colonic polyp classification and the huge amount of endoscopic polyp databases used for our experiments assures a high significance of the achieved results.

Small colorectal polyps

Small (<10 mm) and diminutive (<6 mm) polyps harbour high-grade dysplasia or cancer in 0.3-5% of cases. The potential to grow and develop advanced histology is low. Traditional guidelines still recommend the removal of all polyps. Visual characterisation with modern endoscopic technology could enable us to leave diminutive hyperplastic polyps in situ and remove but discard small polyps. In expert hands, high-definition white-light endoscopy and virtual chromoendoscopy can reach an accuracy of more than 90% in distinguishing between hyperplastic and adenomatous pathology. For less experienced endoscopists the values are lower and therefore the concept is not yet fit for routine use. Polyps can be removed completely with snares but not with forceps. The cold snaring technique in particular has proved safe and effective for small polyps. With more experience in the future a 'cut and discard' strategy for small polyps and a 'do not resect' strategy for diminutive polyps will save money and time to deal with more advanced lesions.

Virtual colon flattening method based on colonic outer surface

Virtual colon flattening (VF) is a minimally invasive viewing mode used to detect colorectal polyps on the colonic inner surface in virtual colonoscopy. Compared with conventional colonoscopy, inspecting a flattened colonic inner surface is faster and results in fewer uninspected regions. Unfortunately, the deformation distortions of flattened colonic inner surface impede the performance of VF. Conventionally, the deformation distortions can be corrected by using the colonic inner surface. However, colonic curvatures and haustral folds make correcting deformation distortions using only the colonic inner surface difficult. Therefore, we propose a VF method that is based on the colonic outer surface. The proposed method includes two novel algorithms, namely, the colonic outer surface extraction algorithm and the colonic outer surface-based distortion correction algorithm. Sixty scans involving 77 annotated polyps were used for the validation. The flattened colons were independently inspected by three operators and then compared with three existing VF methods. The correct detection rates of the proposed method and the three existing methods were 79.6%, 67.1%, 71.9%, and 72.7%, respectively, and the false positives per scan were 0.16, 0.32, 0.21, and 0.26, respectively. The experimental results demonstrate that our proposed method has better performance than existing methods that are based on the colonic inner surface.

Magnifying chromoendoscopy for estimation of lesion histology and shape in colorectal neoplasia

AIM: To assess the application value of magnifying chromo-endoscopy in estimation of lesion histology and shape in colorectal neoplasia.

METHODS: Ninety-one patients with colorectal neoplasia who underwent endoscopic mucosal resection at the Affiliated Hospital of Chengde Medical College from March 2010 to February 2013 were included. Depressed lesions were classified into type 1 and type 2 according to the morphology of depressive areas in colorectal neoplasia by magnifying chromoendoscopy. The relationship between morphologic classification by histology and shape was studied with reference to pathological diagnosis after endoscopic mucosal resection.

RESULTS: Lesions of different histological classification showed no statistical difference in the distribution of morphology (P < 0.05). Type 2 depressive lesions were more susceptible to high grade dysplasia or cancer than type 1 lesions (81.3% vs 31.0%, χ² = 10.405, P = 0.001). The percentage of type 1 lesions with superficial mucosal or submucosal infiltration was statistically more than that of type 2 lesions (92.6% vs 50.0%, χ² = 10.608, P = 0.001).

CONCLUSION: The morphology of superficial depression revealed by magnifying chromoendoscopy in colorectal neoplasia is highly correlated with the histology and invasive depth, which provides a basis for endoscopic mucosal resection.