Scattering features for lung cancer detection in fibered confocal fluorescence microscopy images

The role of confocal laser endomicroscopy in pulmonary medicine

Accurate diagnosis and subsequent therapeutic options in pulmonary diseases mainly rely on imaging methods and histological assessment. However, imaging examinations are hampered by the limited spatial resolution of images and most procedures that are related to histological assessment are invasive with associated complications. As a result, a high-resolution imaging technology - confocal laser endomicroscopy (CLE), which is at the forefront and enables real-time microscopic visualisation of the morphologies and architectures of tissues or cells - has been developed to resolve the clinical dilemma pertaining to current techniques. The current evidence has shown that CLE has the potential to facilitate advanced diagnostic capabilities, to monitor and to aid the tailored treatment regime for patients with pulmonary diseases, as well as to expand the horizon for unravelling the mechanism and therapeutic targets of pulmonary diseases. In the future, if CLE can be combined with artificial intelligence, early, rapid and accurate diagnosis will be achieved through identifying the images automatically. As promising as this technique may be, further investigations are required before it can enter routine clinical practice.

On the robustness of machine learning algorithms toward microfluidic distortions for cell classification via on-chip fluorescence microscopy

Single-cell imaging and sorting are critical technologies in biology and clinical applications. The power of these technologies is increased when combined with microfluidics, fluorescence markers, and machine learning. However, this quest faces several challenges. One of these is the effect of the sample flow velocity on the classification performances. Indeed, cell flow speed affects the quality of image acquisition by increasing motion blur and decreasing the number of acquired frames per sample. We investigate how these visual distortions impact the final classification task in a real-world use-case of cancer cell screening, using a microfluidic platform in combination with light sheet fluorescence microscopy. We demonstrate, by analyzing both simulated and experimental data, that it is possible to achieve high flow speed and high accuracy in single-cell classification. We prove that it is possible to overcome the 3D slice variability of the acquired 3D volumes, by relying on their 2D sum z-projection transformation, to reach an efficient real time classification with an accuracy of 99.4% using a convolutional neural network with transfer learning from simulated data. Beyond this specific use-case, we provide a web platform to generate a synthetic dataset and to investigate the effect of flow speed on cell classification for any biological samples and a large variety of fluorescence microscopes (https://www.creatis.insa-lyon.fr/site7/en/MicroVIP).

Solitary pulmonary nodule imaging approaches and the role of optical fibre-based technologies

Solitary pulmonary nodules (SPNs) are a clinical challenge, given there is no single clinical sign or radiological feature that definitively identifies a benign from a malignant SPN. The early detection of lung cancer has a huge impact on survival outcome. Consequently, there is great interest in the prompt diagnosis, and treatment of malignant SPNs. Current diagnostic pathways involve endobronchial/transthoracic tissue biopsies or radiological surveillance, which can be associated with suboptimal diagnostic yield, healthcare costs and patient anxiety. Cutting-edge technologies are needed to disrupt and improve, existing care pathways. Optical fibre-based techniques, which can be delivered via the working channel of a bronchoscope or via transthoracic needle, may deliver advanced diagnostic capabilities in patients with SPNs. Optical endomicroscopy, an autofluorescence-based imaging technique, demonstrates abnormal alveolar structure in SPNs in vivo Alternative optical fingerprinting approaches, such as time-resolved fluorescence spectroscopy and fluorescence-lifetime imaging microscopy, have shown promise in discriminating lung cancer from surrounding healthy tissue. Whilst fibre-based Raman spectroscopy has enabled real-time characterisation of SPNs in vivo Fibre-based technologies have the potential to enable in situ characterisation and real-time microscopic imaging of SPNs, which could aid immediate treatment decisions in patients with SPNs. This review discusses advances in current imaging modalities for evaluating SPNs, including computed tomography (CT) and positron emission tomography-CT. It explores the emergence of optical fibre-based technologies, and discusses their potential role in patients with SPNs and suspected lung cancer.

Image computing for fibre-bundle endomicroscopy: A review

Endomicroscopy is an emerging imaging modality, that facilitates the acquisition of in vivo, in situ optical biopsies, assisting diagnostic and potentially therapeutic interventions. While there is a diverse and constantly expanding range of commercial and experimental optical biopsy platforms available, fibre-bundle endomicroscopy is currently the most widely used platform and is approved for clinical use in a range of clinical indications. Miniaturised, flexible fibre-bundles, guided through the working channel of endoscopes, needles and catheters, enable high-resolution imaging across a variety of organ systems. Yet, the nature of image acquisition though a fibre-bundle gives rise to several inherent characteristics and limitations necessitating novel and effective image pre- and post-processing algorithms, ranging from image formation, enhancement and mosaicing to pathology detection and quantification. This paper introduces the underlying technology and most prevalent clinical applications of fibre-bundle endomicroscopy, and provides a comprehensive, up-to-date, review of relevant image reconstruction, analysis and understanding/inference methodologies. Furthermore, current limitations as well as future challenges and opportunities in fibre-bundle endomicroscopy computing are identified and discussed.

Functional endoscopy techniques for tracking stem cell fate

Tracking and monitoring implanted stem cells are essential to maximize benefits and to minimize the side effects of stem cell therapy for personalized or "precision" medicine. Previously, we proposed a comprehensive biological Global Positioning System (bGPS) to track and monitor stem cells in vivo. Magnetic resonance imaging (MRI), positron emission tomography (PET), bioluminescent imaging, fluorescence imaging, and single-photon emission computerized tomography (SPECT) have been utilized to track labeled or genetically-modified cells in vivo in rats and humans. A large amount of research has been dedicated to the design of reporter genes and molecular probes for imaging and the visualization of the biodistribution of the implanted cells in high resolution. On the other hand, optical-based functional imaging, such as photoacoustic imaging (PAI), optical coherence tomography (OCT), and multiphoton microscopy (MPM), has been implemented into small endoscopes to image cells inside the body. The optical fiber allows miniaturization of the imaging probe while maintaining high resolution due to light-based imaging. Upon summarizing the recent progress in the design and application of functional endoscopy techniques for stem cell monitoring, we offer perspectives for the future development of endoscopic molecular imaging tools for in vivo tracking of spatiotemporal changes in subclonal evolution at the single cell level.

Morphological Segmentation Analysis and Texture-based Support Vector Machines Classification on Mice Liver Fibrosis Microscopic Images

Background:

To reduce the intensity of the work of doctors, pre-classification work needs to be issued. In this paper, a novel and related liver microscopic image classification analysis method is proposed.

Objective:

For quantitative analysis, segmentation is carried out to extract the quantitative information of special organisms in the image for further diagnosis, lesion localization, learning and treating anatomical abnormalities and computer-guided surgery. </P><P> Methods: In the current work, entropy-based features of microscopic fibrosis mice’ liver images were analyzed using fuzzy c-cluster, k-means and watershed algorithms based on distance transformations and gradient. A morphological segmentation based on a local threshold was deployed to determine the fibrosis areas of images.

Results:

The segmented target region using the proposed method achieved high effective microscopy fibrosis images segmenting of mice liver in terms of the running time, dice ratio and precision. The image classification experiments were conducted using Gray Level Co-occurrence Matrix (GLCM). The best classification model derived from the established characteristics was GLCM which performed the highest accuracy of classification using a developed Support Vector Machine (SVM). The training model using 11 features was found to be accurate when only trained by 8 GLCMs.

Conclusion:

The research illustrated that the proposed method is a new feasible research approach for microscopy mice liver image segmentation and classification using intelligent image analysis techniques. It is also reported that the average computational time of the proposed approach was only 2.335 seconds, which outperformed other segmentation algorithms with 0.8125 dice ratio and 0.5253 precision.</P>

Supervised Image Classification by Scattering Transform with Application to Weed Detection in Culture Crops of High Density

In this article, we assess the interest of the recently introduced multiscale scattering transform for texture classification applied for the first time in plant science. Scattering transform is shown to outperform monoscale approaches (gray-level co-occurrence matrix, local binary patterns) but also multiscale approaches (wavelet decomposition) which do not include combinatory steps. The regime in which scatter transform also outperforms a standard CNN architecture in terms of data-set size is evaluated ( 10 4 instances). An approach on how to optimally design the scatter transform based on energy contrast is provided. This is illustrated on the hard and open problem of weed detection in culture crops of high density from the top view in intensity images. An annotated synthetic data-set available under the form of a data challenge and a simulator are proposed for reproducible science (https://uabox.univ-angers.fr/index.php/s/iuj0knyzOUgsUV9). Scatter transform only trained on synthetic data shows an accuracy of 85 % when tested on real data.

Medical Image Classification Based on Deep Features Extracted by Deep Model and Statistic Feature Fusion with Multilayer Perceptron‬

Medical image classification is a key technique of Computer-Aided Diagnosis (CAD) systems. Traditional methods rely mainly on the shape, color, and/or texture features as well as their combinations, most of which are problem-specific and have shown to be complementary in medical images, which leads to a system that lacks the ability to make representations of high-level problem domain concepts and that has poor model generalization ability. Recent deep learning methods provide an effective way to construct an end-to-end model that can compute final classification labels with the raw pixels of medical images. However, due to the high resolution of the medical images and the small dataset size, deep learning models suffer from high computational costs and limitations in the model layers and channels. To solve these problems, in this paper, we propose a deep learning model that integrates Coding Network with Multilayer Perceptron (CNMP), which combines high-level features that are extracted from a deep convolutional neural network and some selected traditional features. The construction of the proposed model includes the following steps. First, we train a deep convolutional neural network as a coding network in a supervised manner, and the result is that it can code the raw pixels of medical images into feature vectors that represent high-level concepts for classification. Second, we extract a set of selected traditional features based on background knowledge of medical images. Finally, we design an efficient model that is based on neural networks to fuse the different feature groups obtained in the first and second step. We evaluate the proposed approach on two benchmark medical image datasets: HIS2828 and ISIC2017. We achieve an overall classification accuracy of 90.1% and 90.2%, respectively, which are higher than the current successful methods.

Role of artificial intelligence in the care of patients with nonsmall cell lung cancer

BACKGROUND

Lung cancer is the leading cause of cancer death worldwide. In up to 57% of patients, it is diagnosed at an advanced stage and the 5-year survival rate ranges between 10%-16%. There has been a significant amount of research using machine learning to generate tools using patient data to improve outcomes.

METHODS

This narrative review is based on research material obtained from PubMed up to Nov 2017. The search terms include "artificial intelligence," "machine learning," "lung cancer," "Nonsmall Cell Lung Cancer (NSCLC)," "diagnosis" and "treatment."

RESULTS

Recent studies support the use of computer-aided systems and the use of radiomic features to help diagnose lung cancer earlier. Other studies have looked at machine learning (ML) methods that offer prognostic tools to doctors and help them in choosing personalized treatment options for their patients based on molecular, genetics and histological features. Combining artificial intelligence approaches into health care may serve as a beneficial tool for patients with NSCLC, and this review outlines these benefits and current shortcomings throughout the continuum of care.

CONCLUSION

We present a review of the various applications of ML methods in NSCLC as it relates to improving diagnosis, treatment and outcomes.

[Application of Interventional Bronchoscopy in Pulmonary Peripheral Lesions]

肺癌是全球癌症相关死亡的主要原因, 肺癌的治愈率很低, 不仅因其自身攻击性, 还因对肺癌筛查的忽视。随着肺部筛查手段的不断进展, 肺外周病变的检出率逐渐提高, 当前对外周肺病变进行诊断的最常用方法是经支气管行支气管镜检查或计算机断层扫描(computed tomography, CT)引导下经皮穿刺针吸/活检, 然而对于外周肺病灶, 支气管镜检查有较低的诊断率, 经皮穿刺检查有较高的气胸发生率, 因此, 使用安全、微创的方法对外周肺病变进行组织确诊是临床工作者将面临的挑战。新型支气管镜介入诊断技术已逐渐用于临床, 这些技术可有效提高外周肺病变的诊断率, 缩短诊断时间, 使患者获得及时有效的治疗。本文将现有的技术进行简要综述以帮助临床医生尝试应用这些微创技术。

Lung cancer screening: detected nodules, what next?

Since the success of the NLST study, the incorporation of lung cancer screening programs into current academic programs has been growing. Center for Medicare and Medicaid Services have acknowledged the importance and potential impact of lung cancer screening by making it a reimbursable study. Based on Fleischner Society Guidelines, many nodules will require follow-up imaging. The remainder of those nodules will need tissue to appropriately make the diagnosis. The use of bronchoscopy with transbronchial biopsy has been a standard technique for many years, but as smaller nodules need to be assessed, more advanced tools, such as endobronchial ultrasound and electromagnetic navigation are now improving the yield on the diagnosis of these smaller peripheral nodules. As electromagnetic navigation and peripheral ultrasound are significant changes from practice only 10 years ago, further advancements in the technology, such as bronchoscopic robots and advanced optical imaging tools, that are becoming available, need to be assessed as to their possible incorporation into the evaluation of peripheral nodules. The ceiling to the diagnosis of these small lesions remains at 70-75%; techniques and tools need to be used to improve upon this to maximize the impact of lung cancer screening and minimize the risk to patients.

Assessing the utility of autofluorescence-based pulmonary optical endomicroscopy to predict the malignant potential of solitary pulmonary nodules in humans

Solitary pulmonary nodules are common, often incidental findings on chest CT scans. The investigation of pulmonary nodules is time-consuming and often leads to protracted follow-up with ongoing radiological surveillance, however, clinical calculators that assess the risk of the nodule being malignant exist to help in the stratification of patients. Furthermore recent advances in interventional pulmonology include the ability to both navigate to nodules and also to perform autofluorescence endomicroscopy. In this study we assessed the efficacy of incorporating additional information from label-free fibre-based optical endomicrosopy of the nodule on assessing risk of malignancy. Using image analysis and machine learning approaches, we find that this information does not yield any gain in predictive performance in a cohort of patients. Further advances with pulmonary endomicroscopy will require the addition of molecular tracers to improve information from this procedure.

Scalable gastroscopic video summarization via similar-inhibition dictionary selection

OBJECTIVE

This paper aims at developing an automated gastroscopic video summarization algorithm to assist clinicians to more effectively go through the abnormal contents of the video.

METHODS AND MATERIALS

To select the most representative frames from the original video sequence, we formulate the problem of gastroscopic video summarization as a dictionary selection issue. Different from the traditional dictionary selection methods, which take into account only the number and reconstruction ability of selected key frames, our model introduces the similar-inhibition constraint to reinforce the diversity of selected key frames. We calculate the attention cost by merging both gaze and content change into a prior cue to help select the frames with more high-level semantic information. Moreover, we adopt an image quality evaluation process to eliminate the interference of the poor quality images and a segmentation process to reduce the computational complexity.

RESULTS

For experiments, we build a new gastroscopic video dataset captured from 30 volunteers with more than 400k images and compare our method with the state-of-the-arts using the content consistency, index consistency and content-index consistency with the ground truth. Compared with all competitors, our method obtains the best results in 23 of 30 videos evaluated based on content consistency, 24 of 30 videos evaluated based on index consistency and all videos evaluated based on content-index consistency.

CONCLUSIONS

For gastroscopic video summarization, we propose an automated annotation method via similar-inhibition dictionary selection. Our model can achieve better performance compared with other state-of-the-art models and supplies more suitable key frames for diagnosis. The developed algorithm can be automatically adapted to various real applications, such as the training of young clinicians, computer-aided diagnosis or medical report generation.