A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Penitsilliinide jääkide määramine piimas läbivoolulise biosensorsüsteemi abil

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Penitsilliinid on beetalaktaamide hulka kuuluvad antibiootikumid, mida kasutatakse peamiselt Gram-positiivsete bakterite poolt tekitatud haiguste raviks. Penitsilliini ja teiste antibiootikumide jäägid piimas tekitavad inimestel allergilisi reaktsioone ning soodustavad resistentsete mikroobitüvede teket, mistõttu on antibiootikumide jääkide lubatud sisaldus toidus rangelt reguleeritud. Tavaliselt kasutatakse antibiootikumide jääkide määramiseks piimas mitmesuguseid kromatograafial põhinevaid meetodeid ning erinevaid mikroobse inhibeerimise ja immuunoretseptor teste. Kuid tihtipeale on need meetodid kallid ning aeganõudvad. Üheks võimalikuks alternatiiviks traditsioonilistele analüüsimeetoditele on biosensorite kasutamine. Biosensorite eeliseks on nende lihtsus, suhteline odavus ning kiirus, mis võimaldab nende kasutamist kiireteks analüüsideks reaalajas. Käesoleva doktoritöö eesmärgiks oli välja töötada ning testida biosensorsüsteem penitsilliinide jääkide kiireks määramiseks toorpiimas. Selleks, et kiirendada eksperimentaalsete andmete analüüsi, me pakkusime välja matemaatiline mudel biosensori kalibratsiooniparameetrite arvutamiseks ning uurisime võimalusi biosensorites toimuvate äratundmisreaktsioonide kiirendamiseks. Töö praktilises osas testisime biosensori kasutamist penitsilliinide jääkide kiireks määramiseks toorpiimas. Töö tulemusena leidsime, et glükoosi tase piimas on heaks indikaatoriks penitsilliini jääkide määramiseks toorpiimas. Seega, kasutatud biosensorsüsteem on rakendatav kiireks penitsillinide jääkide määramiseks toorpiimas piimafarmides. Kiire lüpstava piima analüüs võimaldaks mittekvaliteetse piima õigeaegset eraldamist kvaliteetsest toodangust ning kogu toodetava piima kvaliteedi tõstmist. Kasutatud biosensorsüsteemi on erinevate antibiotikumide jääkide määramiseks toorpiimas võimalik tulevikus modifitseerida täiendavate biosensorite lisamisega.The use of antibiotics for the treatment of food-producing animals generates the risk to human health due to the transmission of the residues and metabolites of these compounds into food chain. In addition, scientists and health experts also fear that wide application of antimicrobial agents, including the first discovered penicillin antibiotics, is contributing to the rise and spread of antibiotic-resistant bacteria. At present, strict regulations have been established for the levels of antibiotic residues and metabolites in food of animal origin. Antibiotic residues in food are commonly determined with the help of chromatography and special tests. The application of biosensors for the detection of antibiotic residues in milk is a good alternative to traditional methods. The benefits of biosensors are their low cost, simplicity and possibility for rapid real-time analysis. The main goal of the present work was to propose a rapid method for real-time detection of penicillins’ residues in milk, to propose a simple but sufficiently accurate model to describe the quick response of biosensor and to test this biosensorsystem. The application of the model allowed to predict optimal biosensor parameters for obtaining maximal sensitivity and high stability on one hand, and to obtain fast results from the initial phase of the reaction on the other hand. The biosensor was applied to detect the penicillins in the milk of cows with mastitis. Glucose concentration in their milk decreased significantly compared to glucose levels in high quality milk, enabling to use glucose concentration as an indicator of the presence of penicillin residues in milk. The studied biosensor set-up has high potential to serve as a system for real-time automatic control of the quality of raw milk in the milk production farms. The application of this system allows the separation of substandard milk from the milk flow prior to milk collection tank. The system can be further modified by attaching additional biosensors to build up a more robust biosensor array, where the signals of individual biosensors form a typical pattern of milk sample, which changes in the presence of different antibiotics

Silicon-Based Integrated Label-Free Optofluidic Biosensors: Latest Advances and Roadmap

By virtue of the well-developed micro- and nanofabrication technologies and rapidly progressing surface functionalization strategies, silicon-based devices have been widely recognized as a highly promising platform for the next-generation lab-on-a-chip bioanalytical systems with a great potential for point-of-care medical diagnostics. Herein, an overview of the latest advances in silicon-based integrated optofluidic label-free biosensing technologies relying on the efficient interactions between the evanescent light field at the functionalized surface and specifically bound analytes is presented. State-of-the-art technologies demonstrating label-free evanescent wave-based biomarker detection mainly encompass three device configurations, including on-chip waveguide-based interferometers, microring resonators, and photonic-crystal-based cavities. Moreover, up-to-date strategies for elevating the sensitivities and also simplifying the sensing processes are discussed. Emerging laboratory prototypes with advanced integration and packaging schemes incorporating automatic microfluidic components or on-chip optoelectronic devices lead to one significant step forward in real applications of decentralized diagnostics. Besides, particular attention is paid to currently commercialized label-free optical bioanalytical models on the market. Finally, the prospects are elaborated with several research routes toward chip-scale, low-cost, highly sensitive, multi-functional, and user-friendly bioanalytical systems benefiting to global healthcare. © 2020 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context

We present an efficient deep learning approach for the challenging task of tumor segmentation in multisequence MR images. In recent years, Convolutional Neural Networks (CNN) have achieved state-of-the-art performances in a large variety of recognition tasks in medical imaging. Because of the considerable computational cost of CNNs, large volumes such as MRI are typically processed by subvolumes, for instance slices (axial, coronal, sagittal) or small 3D patches. In this paper we introduce a CNN-based model which efficiently combines the advantages of the short-range 3D context and the long-range 2D context. To overcome the limitations of specific choices of neural network architectures, we also propose to merge outputs of several cascaded 2D-3D models by a voxelwise voting strategy. Furthermore, we propose a network architecture in which the different MR sequences are processed by separate subnetworks in order to be more robust to the problem of missing MR sequences. Finally, a simple and efficient algorithm for training large CNN models is introduced. We evaluate our method on the public benchmark of the BRATS 2017 challenge on the task of multiclass segmentation of malignant brain tumors. Our method achieves good performances and produces accurate segmentations with median Dice scores of 0.918 (whole tumor), 0.883 (tumor core) and 0.854 (enhancing core). Our approach can be naturally applied to various tasks involving segmentation of lesions or organs.Comment: Submitted to the journal Computerized Medical Imaging and Graphic

Self-Playing Labyrinth Game Using Camera and Industrial Control System

In this master’s thesis, an industrial control system together with a network camera and servo motors were used to automate a ball and plate labyrinth system. The two servo motors, each with its own servo drive, were connected by joint arms to the plate resting on two interconnected gimbal frames, one for each axis. A background subtraction-based ball position tracking algorithm was developed to measure the ball-position using the camera. The camera acted as a sensor node in a control network with a programmable logical controller used together with the servo drives to implement a cascaded PID control loop to control the ball position. The ball reference position could either be controlled with user input from a tablet device, or automatically to make the labyrinth self-playing. The resulting system was able to control the ball position through the labyrinth using the camera for position feedback

Data-based fault detection in chemical processes: Managing records with operator intervention and uncertain labels

Developing data-driven fault detection systems for chemical plants requires managing uncertain data labels and dynamic attributes due to operator-process interactions. Mislabeled data is a known problem in computer science that has received scarce attention from the process systems community. This work introduces and examines the effects of operator actions in records and labels, and the consequences in the development of detection models. Using a state space model, this work proposes an iterative relabeling scheme for retraining classifiers that continuously refines dynamic attributes and labels. Three case studies are presented: a reactor as a motivating example, flooding in a simulated de-Butanizer column, as a complex case, and foaming in an absorber as an industrial challenge. For the first case, detection accuracy is shown to increase by 14% while operating costs are reduced by 20%. Moreover, regarding the de-Butanizer column, the performance of the proposed strategy is shown to be 10% higher than the filtering strategy. Promising results are finally reported in regard of efficient strategies to deal with the presented problemPeer ReviewedPostprint (author's final draft

High-Performance Accelerometer Based On Asymmetric Gapped Cantilevers For Physiological Acoustic Sensing

Continuous or mobile monitoring of physiological sounds is expected to play important role in the emerging mobile healthcare field. Because of the miniature size, low cost, and easy installation, accelerometer is an excellent choice for continuous physiological acoustic signal monitoring. However, in order to capture the detailed information in the physiological signals for clinical diagnostic purpose, there are more demanding requirements on the sensitivity/noise performance of accelerometers. In this thesis, a unique piezoelectric accelerometer based on the asymmetric gapped cantilever which exhibits significantly improved sensitivity is extensively studied. A meso-scale prototype is developed for capturing the high quality cardio and respiratory sounds on healthy people as well as on heart failure patients. A cascaded gapped cantilever based accelerometer is also explored for low frequency vibration sensing applications such as ballistocardiogram monitoring. Finally, to address the power issues of wireless sensors such as wireless wearable health monitors, a wide band vibration energy harvester based on a folded gapped cantilever is developed and demonstrated on a ceiling air condition unit

Dynamic low-level context for the detection of mild traumatic brain injury.

Mild traumatic brain injury (mTBI) appears as low contrast lesions in magnetic resonance (MR) imaging. Standard automated detection approaches cannot detect the subtle changes caused by the lesions. The use of context has become integral for the detection of low contrast objects in images. Context is any information that can be used for object detection but is not directly due to the physical appearance of an object in an image. In this paper, new low-level static and dynamic context features are proposed and integrated into a discriminative voxel-level classifier to improve the detection of mTBI lesions. Visual features, including multiple texture measures, are used to give an initial estimate of a lesion. From the initial estimate novel proximity and directional distance, contextual features are calculated and used as features for another classifier. This feature takes advantage of spatial information given by the initial lesion estimate using only the visual features. Dynamic context is captured by the proposed posterior marginal edge distance context feature, which measures the distance from a hard estimate of the lesion at a previous time point. The approach is validated on a temporal mTBI rat model dataset and shown to have improved dice score and convergence compared to other state-of-the-art approaches. Analysis of feature importance and versatility of the approach on other datasets are also provided

Detecting, segmenting and tracking bio-medical objects

Studying the behavior patterns of biomedical objects helps scientists understand the underlying mechanisms. With computer vision techniques, automated monitoring can be implemented for efficient and effective analysis in biomedical studies. Promising applications have been carried out in various research topics, including insect group monitoring, malignant cell detection and segmentation, human organ segmentation and nano-particle tracking. In general, applications of computer vision techniques in monitoring biomedical objects include the following stages: detection, segmentation and tracking. Challenges in each stage will potentially lead to unsatisfactory results of automated monitoring. These challenges include different foreground-background contrast, fast motion blur, clutter, object overlap and etc. In this thesis, we investigate the challenges in each stage, and we propose novel solutions with computer vision methods to overcome these challenges and help automatically monitor biomedical objects with high accuracy in different cases --Abstract, page iii