非拘束状態動物のナビゲーション行動を計測及び推定するためのビジョンシステム

要約のみTohoku University橋本浩一課

Prediction of multidrug-resistant TB from CT pulmonary images based on deep learning techniques

While tuberculosis (TB) disease was discovered more than a century ago, it has not been eradicated yet. Quite contrary, at present, TB constitutes one of top 10 causes of death and has shown signs of increasing. To complement conventional diagnostic procedure of applying microbiological culture that takes several weeks and remains expensive, high resolution computer tomography (CT) of pulmonary images has been resorted to not only for aiding clinicians to expedite the process of diagnosis but also for monitoring prognosis when administrating antibiotic drugs. This research undertakes the investigation of predicting multi-drug resistant (MDR) patients from drug sensitive (DS) ones based on CT lung images to monitor the effectiveness of treatment. To contend with smaller datasets (i.e. in hundreds) and the characteristics of CT TB images with limited regions capturing abnormities, patch-based deep convolutional neural network (CNN) allied to support vector machine (SVM) classifier is implemented on a collection of datasets from 230 patients obtained from ImageCLEF 2017 competition. As a result, the proposed architecture of CNN+SVM+patch performs the best with classification accuracy rate at 91.11% (79.80% in terms of patches). In addition, hand-crafted SIFT based approach accomplishes 88.88% in terms of subject and 83.56% with reference to patches, the highest in this study, which can be explained away by the fact that the datasets are in small numbers. Significantly, during the Tuberculosis Competition at ImageCLEF 2017, the authors took part in the task of classification of 5 types of TB disease and achieved top one with regard to averaged classification accuracy (i.e. ACC = 0.4067), which is also premised on the approach of CNN+SVM+patch. On the other hand, when the whole slices of 3D TB datasets are applied to train a CNN network, the best result is achieved through the application of CNN coupled with orderless pooling and SVM at 64.71% accuracy rate

Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture

This research investigates the application of CT pulmonary images to the detection and characterisation of TB at five levels of severity, in order to monitor the efficacy of treatment. To contend with smaller datasets (i.e. in hundreds) and the characteristics of CT TB images in which abnormalities occupy only limited regions, a 3D block-based residual deep learning network (ResNet) coupled with injection of depth information (depth-Resnet) at each layer was implemented. Progress in evaluation has been accomplished in two ways. One is to assess the proposed depth-Resnet in prediction of severity scores and another is to analyse the probability of high severity of TB. For the former, delivered results are of 92.70 ± 5.97% and 67.15 ± 1.69% for proposed depth-Resnet and ResNet-50 respectively. For the latter, two additional measures are put forward, which are calculated using (1) the overall severity (1 to 5) probability, and (2) separate probabilities of both high severity (scores of 1 to 3) and low severity (scores of 4 and 5) respectively, when scores of 1 to 5 are mapped into initial probabilities of (0.9, 0.7, 0.5, 0.3, 0.2) respectively. As a result, these measures achieve the averaged accuracies of 75.88% and 85.29% for both methods respectively

Automatic Identification of Algae using Low-cost Multispectral Fluorescence Digital Microscopy, Hierarchical Classification & Deep Learning

Harmful algae blooms (HABs) can produce lethal toxins and are a rising global concern. In response to this threat, many organizations are monitoring algae populations to determine if a water body might be contaminated. However, identifying algae types in a water sample requires a human expert, a taxonomist, to manually identify organisms using an optical microscope. This is a tedious, time-consuming process that is prone to human error and bias. Since many facilities lack on-site taxonomists, they must ship their water samples off site, further adding to the analysis time. Given the urgency of this problem, this thesis hypothesizes that multispectral fluorescence microscopy with a deep learning hierarchical classification structure is the optimal method to automatically identify algae in water on-site. To test this hypothesis, a low-cost system was designed and built which was able generate one brightfield image and four fluorescence images. Each of the four fluorescence images was designed to target a different pigment in algae, resulting in a unique autofluorescence spectral fingerprint for different phyla groups. To complement this hardware system, a software framework was designed and developed. This framework used the prior taxonomic structure of algae to create a hierarchical classification structure. This hierarchical classifier divided the classification task into three steps which were phylum, genus, and species level classification. Deep learning models were used at each branch of this hierarchical classifier allowing the optimal set of features to be implicitly learned from the input data. In order to test the efficacy of the proposed hardware system and corresponding software framework, a dataset of nine algae from 4 different phyla groups was created. A number of preprocessing steps were required to prepare the data for analysis. These steps were flat field correction, thresholding and cropping. With this multispectral imaging data, a number of spatial and spectral features were extracted for use in the feature-extraction-based models. This dataset was used to determine the relative performance of 12 different model architectures, and the proposed multispectral hierarchical deep learning approach achieved the top classification accuracy of 97% to the species level. Further inspection revealed that a traditional feature extraction method was able to achieve 95% to the phyla level when only using the multispectral fluorescence data. These observations strongly support that: (1) the proposed low-cost multispectral fluorescence imaging system, and (2) the proposed hierarchical structure based on the taxonomy prior, in combination with (3) deep learning methods for feature learning, is an effective method to automatically classify algae

Canny and Morphological Approaches to Calculating Area and Perimeter of Two-Dimensional Geometry

Calculating area and perimeter in real-world conditions has its challenges. The actual conditions include applications in the medical field to measure the presence of tumors or the condition of human organs and applications in geography to measure specific areas on a map; applications in architecture often calculate the area and perimeter of buildings, interior design, exterior design, and other uses. Technology can make it easier with automatic calculations. Mathematical methods and computer vision techniques are required to create automated systems. The Canny method is usually used, which is good enough for detecting edges but not sufficient for measuring irregular geometric shapes. This paper aims to calculate the area and perimeter of a geometric shape using the Canny method and geometry. Data samples in various forms are used in this study. Calculating area and perimeter using the Canny method involves obtaining the length (X,Y) of the RGB image converted to HSV. Edge detection values are used to calculate the area and perimeter of objects. The morphological method uses binary image input as input data. Then proceed to the convolution process with structuring and calculating the area and circumference of objects. Based on the research results, calculating the area and circumference of objects is more effective using morphological methods. However, the level of accuracy is affected by the selection of structuring elements (strels) which must be optimal and global

An empirical study on ensemble of segmentation approaches

Riconoscere oggetti all’interno delle immagini richiede delle abilità complesse che richiedono una conoscenza del contesto e la capacità di identificare i bordi degli oggetti stessi. Nel campo della computer vision, questo compito è chiamato segmentazione semantica e riguarda la classificazione di ogni pixel all’interno di un’immagine. Tale compito è di primaria importanza in molti scenari reali: nei veicoli autonomi, dove permette l’identificazione degli oggetti che circondano il veicolo, o nella diagnosi medica, in cui migliora la capacità di identificare patologie pericolose e quindi mitigare il rischio di serie conseguenze. In questo studio, proponiamo un nuovo modello per un multiclassificatore in grado di risolvere il compito di segmentazione semantica. Il modello si basa su reti neurali convoluzionali (CNN) e transformers. Un multiclassificatore usa diversi modelli le cui stime vengono aggregate così da ottenere l’output del sistema di multiclassificazione. Le prestazioni e la qualità delle previsioni dell’ensemble sono fortemente connessi ad alcuni fattori, tra cui il più importante è la diversità tra i singoli modelli. Nell’approccio qui proposto, abbiamo ottenuto questo risultato adottando diverse loss functions e testando con diversi metodi di data augmentation. Abbiamo sviluppato questo metodo combinando DeepLabV3+, HarDNet-MSEG e dei Pyramid Vision Transformers (PVT). La soluzione qui sviluppata è stata poi esaminata mediante un’ampia valutazione empirica in 5 diversi scenari: rilevamento di polipi, rilevamento della pelle, riconoscimento di leucociti, rilevamento di microorganismi e riconoscimento di farfalle. Il modello fornisce dei risultati che sono allo stato dell’arte. Tutte le risorse sono disponibili online all’indirizzo https://github.com/AlbertoFormaggio1/Ensemble-Of-Segmentation.Recognizing objects in images requires complex skills that involve knowledge about the context and the ability to identify the borders of the objects. In computer vision, this task is called semantic segmentation and it pertains to the classification of each pixel in an image. The task is of main importance in many real-life scenarios: in autonomous vehicles, it allows the identification of objects surrounding the vehicle; in medical diagnosis, it improves the ability of early detecting dangerous pathologies and thus to mitigate the risk of serious consequences. In this work, we propose a new ensemble method able to solve the semantic segmentation task. The model is based on convolutional neural networks (CNNs) and transformers. An ensemble uses many different models whose predictions are aggregated to form the output of the ensemble system. The performance and quality of the ensemble prediction are strongly connected with some factors, one of the most important is the diversity among individual models. In our approach, this is enforced by adopting different loss functions and testing different data augmentation. We developed the proposed method by combining DeepLabV3+, HarDNet-MSEG, and Pyramid Vision Transformers. The developed solution was then assessed through an extensive empirical evaluation in five different scenarios: polyp detection, skin detection, leukocytes recognition, environmental microorganism detection, and butterfly recognition. The model provides state-of-the-art results. All resources will be available online at https://github.com/AlbertoFormaggio1/Ensemble-Of-Segmentation

Human-Centric Machine Vision

Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans