RESUMO
Our aim is to complement observer-dependent approaches of immune cell evaluation in microscopy images with reproducible measures for spatial composition of lymphocytic infiltrates. Analyzing such patterns of inflammation is becoming increasingly important for therapeutic decisions, for example in transplantation medicine or cancer immunology. We developed a graph-based assessment of lymphocyte clustering in full whole slide images. Based on cell coordinates detected in the full image, a Delaunay triangulation and distance criteria are used to build neighborhood graphs. The composition of nodes and edges are used for classification, e.g. using a support vector machine. We describe the variability of these infiltrates on CD3/CD20 duplex staining in renal biopsies of long-term functioning allografts, in breast cancer cases, and in lung tissue of cystic fibrosis patients. The assessment includes automated cell detection, identification of regions of interest, and classification of lymphocytic clusters according to their degree of organization. We propose a neighborhood feature which considers the occurrence of edges with a certain type in the graph to distinguish between phenotypically different immune infiltrates. Our work addresses a medical need and provides a scalable framework that can be easily adjusted to the requirements of different research questions.
Assuntos
Tecido Linfoide/anatomia & histologia , Análise de Célula Única , Neoplasias da Mama/patologia , Feminino , Humanos , Máquina de Vetores de SuporteRESUMO
PURPOSE: To improve microscopic evaluation of immune cells relevant in breast cancer oncoimmunology, we aim at distinguishing normal infiltration patterns from lymphocytic lobulitis by advanced image analysis. We consider potential immune cell variations due to the menstrual cycle and oral contraceptives in non-neoplastic mammary gland tissue. METHODS: Lymphocyte and macrophage distributions were analyzed in the anatomical context of the resting mammary gland in immunohistochemically stained digital whole slide images obtained from 53 reduction mammoplasty specimens. Our image analysis workflow included automated regions of interest detection, immune cell recognition, and co-registration of regions of interest. RESULTS: In normal lobular epithelium, seven CD8[Formula: see text] lymphocytes per 100 epithelial cells were present on average and about 70% of this T-lymphocyte population was lined up along the basal cell layer in close proximity to the epithelium. The density of CD8[Formula: see text] T-cell was 1.6 fold higher in the luteal than in the follicular phase in spontaneous menstrual cycles and 1.4 fold increased under the influence of oral contraceptives, and not co-localized with epithelial proliferation. CD4[Formula: see text] T-cells were infrequent. Abundant CD163[Formula: see text] macrophages were widely spread, including the interstitial compartment, with minor variation during the menstrual cycle. CONCLUSIONS: Spatial patterns of different immune cell subtypes determine the range of normal, as opposed to inflammatory conditions of the breast tissue microenvironment. Advanced image analysis enables quantification of hormonal effects, refines lymphocytic lobulitis, and shows potential for comprehensive biopsy evaluation in oncoimmunology.
Assuntos
Linfócitos/imunologia , Macrófagos/imunologia , Glândulas Mamárias Humanas/anatomia & histologia , Antígenos CD/metabolismo , Antígenos de Diferenciação Mielomonocítica/metabolismo , Linfócitos T CD4-Positivos/metabolismo , Linfócitos T CD8-Positivos/metabolismo , Anticoncepcionais Orais , Feminino , Humanos , Mamoplastia , Glândulas Mamárias Humanas/imunologia , Glândulas Mamárias Humanas/cirurgia , Ciclo Menstrual , Receptores de Superfície Celular/metabolismoRESUMO
OBJECTIVE: Each surgical procedure is unique due to patient's and also surgeon's particularities. In this study, we propose a new approach to distinguish surgical behaviors between surgical sites, levels of expertise and individual surgeons thanks to a pattern discovery method. METHODS: The developed approach aims to distinguish surgical behaviors based on shared longest frequent sequential patterns between surgical process models. To allow clustering, we propose a new metric called SLFSP. The approach is validated by comparison with a clustering method using Dynamic Time Warping as a metric to characterize the similarity between surgical process models. RESULTS: Our method outperformed the existing approach. It was able to make a perfect distinction between surgical sites (accuracy of 100%). We reached an accuracy superior to 90% and 85% for distinguishing levels of expertise and individual surgeons. CONCLUSION: Clustering based on shared longest frequent sequential patterns outperformed the previous study based on time analysis. SIGNIFICANCE: The proposed method shows the feasibility of comparing surgical process models, not only by their duration but also by their structure of activities. Furthermore, patterns may show risky behaviors, which could be an interesting information for surgical training to prevent adverse events.
Assuntos
Competência Clínica , Análise por Conglomerados , Cirurgia Geral/educação , Cirurgia Geral/métodos , Procedimentos Cirúrgicos Operatórios , Humanos , Modelos Anatômicos , Risco , Fatores de TempoAssuntos
Inteligência Artificial , Diagnóstico , Algoritmos , Erros de Diagnóstico , Humanos , Resolução de Problemas , PensamentoRESUMO
This paper describes the creation of a comprehensive conceptualization of object models used in medical image simulation, suitable for major imaging modalities and simulators. The goal is to create an application ontology that can be used to annotate the models in a repository integrated in the Virtual Imaging Platform (VIP), to facilitate their sharing and reuse. Annotations make the anatomical, physiological and pathophysiological content of the object models explicit. In such an interdisciplinary context we chose to rely on a common integration framework provided by a foundational ontology, that facilitates the consistent integration of the various modules extracted from several existing ontologies, i.e. FMA, PATO, MPATH, RadLex and ChEBI. Emphasis is put on methodology for achieving this extraction and integration. The most salient aspects of the ontology are presented, especially the organization in model layers, as well as its use to browse and query the model repository.
Assuntos
Diagnóstico por Imagem , Processamento de Imagem Assistida por Computador/métodos , Internet , Semântica , Vocabulário Controlado , Encéfalo/patologia , Simulação por Computador , Humanos , Modelos Teóricos , SoftwareRESUMO
Surgical Process Modelling (SPM) was introduced to improve understanding the different parameters that influence the performance of a Surgical Process (SP). Data acquired from SPM methodology is enormous and complex. Several analysis methods based on comparison or classification of Surgical Process Models (SPMs) have previously been proposed. Such methods compare a set of SPMs to highlight specific parameters explaining differences between populations of patients, surgeons or systems. In this study, procedures performed at three different international University hospitals were compared using SPM methodology based on a similarity metric focusing on the sequence of activities occurring during surgery. The proposed approach is based on Dynamic Time Warping (DTW) algorithm combined with a clustering algorithm. SPMs of 41 Anterior Cervical Discectomy (ACD) surgeries were acquired at three Neurosurgical departments; in France, Germany, and Canada. The proposed approach distinguished the different surgical behaviors according to the location where surgery was performed as well as between the categorized surgical experience of individual surgeons. We also propose the use of Multidimensional Scaling to induce a new space of representation of the sequences of activities. The approach was compared to a time-based approach (e.g. duration of surgeries) and has been shown to be more precise. We also discuss the integration of other criteria in order to better understand what influences the way the surgeries are performed. This first multi-site study represents an important step towards the creation of robust analysis tools for processing SPMs. It opens new perspectives for the assessment of surgical approaches, tools or systems as well as objective assessment and comparison of surgeon's expertise.
Assuntos
Modelos Biológicos , Procedimentos Neurocirúrgicos , Análise por Conglomerados , HumanosRESUMO
In the creation of new computer-assisted intervention systems, Surgical Process Models (SPMs) are an emerging concept used for analyzing and assessing surgical interventions. SPMs represent Surgical Processes (SPs) which are formalized as symbolic structured descriptions of surgical interventions using a pre-defined level of granularity and a dedicated terminology. In this context, one major challenge is the creation of new metrics for the comparison and the evaluation of SPs. Thus, correlations between these metrics and pre-operative data are used to classify surgeries and highlight specific information on the surgery itself and on the surgeon, such as his/her level of expertise. In this paper, we explore the automatic classification of a set of SPs based on the Dynamic Time Warping (DTW) algorithm. DTW is used to compute a similarity measure between two SPs that focuses on the different types of activities performed during surgery and their sequencing, by minimizing time differences. Indeed, it turns out to be a complementary approach to the classical methods that only focus on differences in the time and the number of activities. Experiments were carried out on 24 lumbar disk herniation surgeries to discriminate the surgeons level of expertise according to a prior classification of SPs. Supervised and unsupervised classification experiments have shown that this approach was able to automatically identify groups of surgeons according to their level of expertise (senior and junior), and opens many perspectives for the creation of new metrics for comparing and evaluating surgeries.
Assuntos
Algoritmos , Deslocamento do Disco Intervertebral/cirurgia , Competência Clínica , Humanos , Informática Médica , Modelos Anatômicos , Cirurgia Assistida por Computador/métodosRESUMO
Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.
Assuntos
Ciência de Dados , Aprendizado de Máquina , HumanosRESUMO
OBJECTIVE: This article presents an automatic image processing framework to extract quantitative high-level information describing the micro-environment of glomeruli in consecutive whole slide images (WSIs) processed with different staining modalities of patients with chronic kidney rejection after kidney transplantation. METHODS: This four-step framework consists of: 1) approximate rigid registration, 2) cell and anatomical structure segmentation 3) fusion of information from different stainings using a newly developed registration algorithm 4) feature extraction. RESULTS: Each step of the framework is validated independently both quantitatively and qualitatively by pathologists. An illustration of the different types of features that can be extracted is presented. CONCLUSION: The proposed generic framework allows for the analysis of the micro-environment surrounding large structures that can be segmented (either manually or automatically). It is independent of the segmentation approach and is therefore applicable to a variety of biomedical research questions. SIGNIFICANCE: Chronic tissue remodelling processes after kidney transplantation can result in interstitial fibrosis and tubular atrophy (IFTA) and glomerulosclerosis. This pipeline provides tools to quantitatively analyse, in the same spatial context, information from different consecutive WSIs and help researchers understand the complex underlying mechanisms leading to IFTA and glomerulosclerosis.
Assuntos
Técnicas Histológicas , Nefropatias , Algoritmos , Humanos , Processamento de Imagem Assistida por Computador , Rim/diagnóstico por imagem , Nefropatias/diagnóstico por imagemRESUMO
PURPOSE: Manual feedback from senior surgeons observing less experienced trainees is a laborious task that is very expensive, time-consuming and prone to subjectivity. With the number of surgical procedures increasing annually, there is an unprecedented need to provide an accurate, objective and automatic evaluation of trainees' surgical skills in order to improve surgical practice. METHODS: In this paper, we designed a convolutional neural network (CNN) to classify surgical skills by extracting latent patterns in the trainees' motions performed during robotic surgery. The method is validated on the JIGSAWS dataset for two surgical skills evaluation tasks: classification and regression. RESULTS: Our results show that deep neural networks constitute robust machine learning models that are able to reach new competitive state-of-the-art performance on the JIGSAWS dataset. While we leveraged from CNNs' efficiency, we were able to minimize its black-box effect using the class activation map technique. CONCLUSIONS: This characteristic allowed our method to automatically pinpoint which parts of the surgery influenced the skill evaluation the most, thus allowing us to explain a surgical skill classification and provide surgeons with a novel personalized feedback technique. We believe this type of interpretable machine learning model could integrate within "Operation Room 2.0" and support novice surgeons in improving their skills to eventually become experts.
Assuntos
Competência Clínica , Retroalimentação , Cirurgia Geral/educação , Cirurgia Geral/instrumentação , Aprendizado de Máquina , Redes Neurais de Computação , Fenômenos Biomecânicos , Análise por Conglomerados , Humanos , Cadeias de Markov , Modelos Estatísticos , Movimento (Física) , Análise de Regressão , Procedimentos Cirúrgicos Robóticos , CirurgiõesRESUMO
Crowdsourcing in pathology has been performed on tasks that are assumed to be manageable by nonexperts. Demand remains high for annotations of more complex elements in digital microscopic images, such as anatomical structures. Therefore, this paper investigates conditions to enable crowdsourced annotations of high-level image objects, a complex task considered to require expert knowledge. Seventy six medical students without specific domain knowledge who voluntarily participated in three experiments solved two relevant annotation tasks on histopathological images: 1) labeling of images showing tissue regions and 2) delineation of morphologically defined image objects. We focus on methods to ensure sufficient annotation quality including several tests on the required number of participants and on the correlation of participants' performance between tasks. In a set up simulating annotation of images with limited ground truth, we validated the feasibility of a confidence score using full ground truth. For this, we computed a majority vote using weighting factors based on individual assessment of contributors against scattered gold standard annotated by pathologists. In conclusion, we provide guidance for task design and quality control to enable a crowdsourced approach to obtain accurate annotations required in the era of digital pathology.
Assuntos
Crowdsourcing/métodos , Histocitoquímica , Estudantes de Medicina , Tomada de Decisões/fisiologia , Estudos de Viabilidade , Histocitoquímica/classificação , Histocitoquímica/métodos , Humanos , Processamento de Imagem Assistida por Computador , Reprodutibilidade dos TestesRESUMO
PURPOSE: Teleoperated robotic systems are nowadays routinely used for specific interventions. Benefits of robotic training courses have already been acknowledged by the community since manipulation of such systems requires dedicated training. However, robotic surgical simulators remain expensive and require a dedicated human-machine interface. METHODS: We present a low-cost contactless optical sensor, the Leap Motion, as a novel control device to manipulate the RAVEN-II robot. We compare peg manipulations during a training task with a contact-based device, the electro-mechanical Sigma.7. We perform two complementary analyses to quantitatively assess the performance of each control method: a metric-based comparison and a novel unsupervised spatiotemporal trajectory clustering. RESULTS: We show that contactless control does not offer as good manipulability as the contact-based. Where part of the metric-based evaluation presents the mechanical control better than the contactless one, the unsupervised spatiotemporal trajectory clustering from the surgical tool motions highlights specific signature inferred by the human-machine interfaces. CONCLUSIONS: Even if the current implementation of contactless control does not overtake manipulation with high-standard mechanical interface, we demonstrate that using the optical sensor complete control of the surgical instruments is feasible. The proposed method allows fine tracking of the trainee's hands in order to execute dexterous laparoscopic training gestures. This work is promising for development of future human-machine interfaces dedicated to robotic surgical training systems.
Assuntos
Procedimentos Cirúrgicos Robóticos/educação , Interface Usuário-Computador , Gestos , Humanos , Procedimentos Cirúrgicos Robóticos/métodosRESUMO
PURPOSE: Surgical processes are generally only studied by identifying differences in populations such as participants or level of expertise. But the similarity between this population is also important in understanding the process. We therefore proposed to study these two aspects. METHODS: In this article, we show how similarities in process workflow within a population can be identified as sequential surgical signatures. To this purpose, we have proposed a pattern mining approach to identify these signatures. VALIDATION: We validated our method with a data set composed of seventeen micro-surgical suturing tasks performed by four participants with two levels of expertise. RESULTS: We identified sequential surgical signatures specific to each participant, shared between participants with and without the same level of expertise. These signatures are also able to perfectly define the level of expertise of the participant who performed a new micro-surgical suturing task. However, it is more complicated to determine who the participant is, and the method correctly determines this information in only 64% of cases. CONCLUSION: We show for the first time the concept of sequential surgical signature. This new concept has the potential to further help to understand surgical procedures and provide useful knowledge to define future CAS systems.
Assuntos
Reconhecimento Automatizado de Padrão , Cirurgia Assistida por Computador , Técnicas de Sutura , Fluxo de Trabalho , HumanosRESUMO
PURPOSE: Surgery is one of the riskiest and most important medical acts that are performed today. The need to improve patient outcomes and surgeon training, and to reduce the costs of surgery, has motivated the equipment of operating rooms with sensors that record surgical interventions. The richness and complexity of the data that are collected call for new methods to support computer-assisted surgery. The aim of this paper is to support the monitoring of junior surgeons learning their surgical skill sets. METHODS: Our method is fully automatic and takes as input a series of surgical interventions each represented by a low-level recording of all activities performed by the surgeon during the intervention (e.g., cut the skin with a scalpel). Our method produces a curve describing the process of standardization of the behavior of junior surgeons. Given the fact that junior surgeons receive constant feedback from senior surgeons during surgery, these curves can be directly interpreted as learning curves. RESULTS: Our method is assessed using the behavior of a junior surgeon in anterior cervical discectomy and fusion surgery over his first three years after residency. They revealed the ability of the method to accurately represent the surgical skill evolution. We also showed that the learning curves can be computed by phases allowing a finer evaluation of the skill progression. CONCLUSION: Preliminary results suggest that our approach constitutes a useful addition to surgical training monitoring.
Assuntos
Vértebras Cervicais/cirurgia , Competência Clínica , Discotomia/educação , Curva de Aprendizado , Fusão Vertebral/educação , Hemostasia Cirúrgica/educação , Humanos , Internato e Residência , Neurocirurgia/educação , Salas Cirúrgicas , Gravação em VídeoRESUMO
OBJECTIVE: The analysis of surgical motion has received a growing interest with the development of devices allowing their automatic capture. In this context, the use of advanced surgical training systems makes an automated assessment of surgical trainee possible. Automatic and quantitative evaluation of surgical skills is a very important step in improving surgical patient care. MATERIAL AND METHOD: In this paper, we present an approach for the discovery and ranking of discriminative and interpretable patterns of surgical practice from recordings of surgical motions. A pattern is defined as a series of actions or events in the kinematic data that together are distinctive of a specific gesture or skill level. Our approach is based on the decomposition of continuous kinematic data into a set of overlapping gestures represented by strings (bag of words) for which we compute comparative numerical statistic (tf-idf) enabling the discriminative gesture discovery via its relative occurrence frequency. RESULTS: We carried out experiments on three surgical motion datasets. The results show that the patterns identified by the proposed method can be used to accurately classify individual gestures, skill levels and surgical interfaces. We also present how the patterns provide a detailed feedback on the trainee skill assessment. CONCLUSIONS: The proposed approach is an interesting addition to existing learning tools for surgery as it provides a way to obtain a feedback on which parts of an exercise have been used to classify the attempt as correct or incorrect.
Assuntos
Gestos , Reconhecimento Automatizado de Padrão/métodos , Procedimentos Cirúrgicos Operatórios/educação , Algoritmos , Fenômenos Biomecânicos , Competência Clínica , Feedback Formativo , Humanos , Análise e Desempenho de Tarefas , Estudos de Tempo e MovimentoRESUMO
PURPOSE: The development of common ontologies has recently been identified as one of the key challenges in the emerging field of surgical data science (SDS). However, past and existing initiatives in the domain of surgery have mainly been focussing on individual groups and failed to achieve widespread international acceptance by the research community. To address this challenge, the authors of this paper launched a European initiative-OntoSPM Collaborative Action-with the goal of establishing a framework for joint development of ontologies in the field of SDS. This manuscript summarizes the goals and the current status of the international initiative. METHODS: A workshop was organized in 2016, gathering the main European research groups having experience in developing and using ontologies in this domain. It led to the conclusion that a common ontology for surgical process models (SPM) was absolutely needed, and that the existing OntoSPM ontology could provide a good starting point toward the collaborative design and promotion of common, standard ontologies on SPM. RESULTS: The workshop led to the OntoSPM Collaborative Action-launched in mid-2016-with the objective to develop, maintain and promote the use of common ontologies of SPM relevant to the whole domain of SDS. The fundamental concept, the architecture, the management and curation of the common ontology have been established, making it ready for wider public use. CONCLUSION: The OntoSPM Collaborative Action has been in operation for 24 months, with a growing dedicated membership. Its main result is a modular ontology, undergoing constant updates and extensions, based on the experts' suggestions. It remains an open collaborative action, which always welcomes new contributors and applications.
Assuntos
Ontologias Biológicas , Procedimentos Cirúrgicos Minimamente Invasivos , Modelos Anatômicos , Reconhecimento Automatizado de Padrão , Europa (Continente) , Humanos , Cooperação InternacionalRESUMO
OBJECTIVE: More than half a million surgeries are performed every day worldwide, which makes surgery one of the most important component of global health care. In this context, the objective of this paper is to introduce a new method for the prediction of the possible next task that a surgeon is going to perform during surgery. MATERIAL AND METHOD: We formulate the problem as finding the optimal registration of a partial sequence to a complete reference sequence of surgical activities. We propose an efficient algorithm to find the optimal partial alignment and a prediction system using maximum a posteriori probability estimation and filtering. We also introduce a weighting scheme allowing to improve the predictions by taking into account the relative similarity between the current surgery and a set of pre-recorded surgeries. RESULTS: Our method is evaluated on two types of neurosurgical procedures: lumbar disc herniation removal and anterior cervical discectomy. Results show that our method outperformed the state of the art by predicting the next task that the surgeon will perform with 95% accuracy. CONCLUSIONS: This work shows that, even from the low-level description of surgeries and without other sources of information, it is often possible to predict the next surgical task when the conditions are consistent with the previously recorded surgeries. We also showed that our method is able to assess when there is actually a large divergence between the predictions and decide that it is not reasonable to make a prediction.
Assuntos
Inteligência Artificial , Vértebras Cervicais/cirurgia , Discotomia , Deslocamento do Disco Intervertebral/cirurgia , Vértebras Lombares/cirurgia , Atividade Motora , Destreza Motora , Sistemas de Informação em Salas Cirúrgicas , Cirurgiões , Automação , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Equipe de Assistência ao Paciente , Análise e Desempenho de Tarefas , Fatores de TempoRESUMO
OBJECTIVE: Surgery is one of the riskiest and most important medical acts that is performed today. Understanding the ways in which surgeries are similar or different from each other is of major interest to understand and analyze surgical behaviors. This article addresses the issue of identifying discriminative patterns of surgical practice from recordings of surgeries. These recordings are sequences of low-level surgical activities representing the actions performed by surgeons during surgeries. MATERIALS AND METHOD: To discover patterns that are specific to a group of surgeries, we use the vector space model (VSM) which is originally an algebraic model for representing text documents. We split long sequences of surgical activities into subsequences of consecutive activities. We then compute the relative frequencies of these subsequences using the tf*idf framework and we use the Cosine similarity to classify the sequences. This process makes it possible to discover which patterns discriminate one set of surgeries recordings from another set. RESULTS: Experiments were performed on 40 neurosurgeries of anterior cervical discectomy (ACD). The results demonstrate that our method accurately identifies patterns that can discriminate between (1) locations where the surgery took place, (2) levels of expertise of surgeons (i.e., expert vs. intermediate) and even (3) individual surgeons who performed the intervention. We also show how the tf*idf weight vector can be used to both visualize the most interesting patterns and to highlight the parts of a given surgery that are the most interesting. CONCLUSIONS: Identifying patterns that discriminate groups of surgeon is a very important step in improving the understanding of surgical processes. The proposed method finds discriminative and interpretable patterns in sequences of surgical activities. Our approach provides intuitive results, as it identifies automatically the set of patterns explaining the differences between the groups.
Assuntos
Vértebras Cervicais/cirurgia , Discotomia/tendências , Disco Intervertebral/cirurgia , Neurocirurgiões/tendências , Reconhecimento Automatizado de Padrão/métodos , Padrões de Prática Médica/tendências , Máquina de Vetores de Suporte , Análise e Desempenho de Tarefas , Algoritmos , Competência Clínica , Discotomia/efeitos adversos , Discotomia/classificação , Humanos , Neurocirurgiões/classificação , Padrões de Prática Médica/classificação , Gravação em VídeoRESUMO
BACKGROUND: Ongoing research into inflammatory conditions raises an increasing need to evaluate immune cells in histological sections in biologically relevant regions of interest (ROIs). Herein, we compare different approaches to automatically detect lobular structures in human normal breast tissue in digitized whole slide images (WSIs). This automation is required to perform objective and consistent quantitative studies on large data sets. METHODS: In normal breast tissue from nine healthy patients immunohistochemically stained for different markers, we evaluated and compared three different image analysis methods to automatically detect lobular structures in WSIs: (1) a bottom-up approach using the cell-based data for subsequent tissue level classification, (2) a top-down method starting with texture classification at tissue level analysis of cell densities in specific ROIs, and (3) a direct texture classification using deep learning technology. RESULTS: All three methods result in comparable overall quality allowing automated detection of lobular structures with minor advantage in sensitivity (approach 3), specificity (approach 2), or processing time (approach 1). Combining the outputs of the approaches further improved the precision. CONCLUSIONS: Different approaches of automated ROI detection are feasible and should be selected according to the individual needs of biomarker research. Additionally, detected ROIs could be used as a basis for quantification of immune infiltration in lobular structures.
Assuntos
Mama/citologia , Processamento de Imagem Assistida por Computador/métodos , Mama/metabolismo , Feminino , Humanos , Imuno-Histoquímica/métodosRESUMO
Dexterity and procedural knowledge are two critical skills that surgeons need to master to perform accurate and safe surgical interventions. However, current training systems do not allow us to provide an in-depth analysis of surgical gestures to precisely assess these skills. Our objective is to develop a method for the automatic and quantitative assessment of surgical gestures. To reach this goal, we propose a new unsupervised algorithm that can automatically segment kinematic data from robotic training sessions. Without relying on any prior information or model, this algorithm detects critical points in the kinematic data that define relevant spatio-temporal segments. Based on the association of these segments, we obtain an accurate recognition of the gestures involved in the surgical training task. We, then, perform an advanced analysis and assess our algorithm using datasets recorded during real expert training sessions. After comparing our approach with the manual annotations of the surgical gestures, we observe 97.4% accuracy for the learning purpose and an average matching score of 81.9% for the fully automated gesture recognition process. Our results show that trainees workflow can be followed and surgical gestures may be automatically evaluated according to an expert database. This approach tends toward improving training efficiency by minimizing the learning curve.