Multi-pathology detection and lesion localization in WCE videos by using the instance segmentation approach

The majority of current systems for automatic diagnosis considers the detection of a unique and previously known pathology. Considering specifically the diagnosis of lesions in the small bowel using endoscopic capsule images, very few consider the possible existence of more than one pathology and when they do, they are mainly detection based systems therefore unable to localize the suspected lesions. Such systems do not fully satisfy the medical community, that in fact needs a system that detects any pathology and eventually more than one, when they coexist. In addition, besides the diagnostic capability of these systems, localizing the lesions in the image has been of great interest to the medical community, mainly for training medical personnel purposes. So, nowadays, the inclusion of the lesion location in automatic diagnostic systems is practically mandatory. Multi-pathology detection can be seen as a multi-object detection task and as each frame can contain different instances of the same lesion, instance segmentation seems to be appropriate for the purpose. Consequently, we argue that a multi-pathology system benefits from using the instance segmentation approach, since classification and segmentation modules are both required complementing each other in lesion detection and localization. According to our best knowledge such a system does not yet exist for the detection of WCE pathologies. This paper proposes a multi-pathology system that can be applied to WCE images, which uses the Mask Improved RCNN (MI-RCNN), a new mask subnet scheme which has shown to significantly improve mask predictions of the high performing state-of-the-art Mask-RCNN and PANet systems. A novel training strategy based on the second momentum is also proposed for the first time for training Mask-RCNN and PANet based systems. These approaches were tested using the public database KID, and the included pathologies were bleeding, angioectasias, polyps and inflammatory lesions. Experimental results show significant improvements for the prFCT national funds, under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020 and through the PhD Grants with the references SFRH/BD/92143/2013 and SFRH/BD/139061/201

Diagnóstico automático de anormalidades no trato urinário

Programa Doutoral em Engenharia BiomédicaA influência de sistemas de imagem médica para auxiliar os diagnósticos e tratamentos tem crescido consideravelmente. Relativamente ao trato urinário, a cistoscopia de luz branca é o procedimento padrão que fornece uma visão direta do seu interior, e é especialmente utilizada para diagnóstico e extração de lesões especialmente nos órgãos do trato urinário baixo, como o cancro de bexiga. Contudo, este é regularmente diagnosticado de forma errónea devido à elevada dependência da experiência do urologista, pelo que as regiões suspeitas carecem de uma posterior análise por biópsia. Esta tese aborda o desenvolvimento de soluções de aprendizagem automática para deteção e classificação de diferentes lesões na bexiga. Para isso foi criada uma base de dados contendo cancros de bexiga e inflamações com regiões de interesse anotadas, e foram desenvolvidos dois algoritmos de aprendizagem. O primeiro método consistiu num modelo de classificação ensemble com fusão de descritores que permite preservar a diversidade, que é propriedade fundamental dos ensembles. Este sistema utiliza diferentes descritores baseados em cor e textura (Local Binary Patterns, Wavelets e Histogram of oriented gradients condicionados pela cor) para melhorar o desempenho de classificadores simples. A utilização conjunta dos descritores e do classificador propostos mostraram capacidades semelhantes às redes de aprendizagem profunda, como é o caso da técnica de transferência de conhecimento e das Capsule Neural Networks. Assim, este modelo poderá ser utilizado em casos onde os dados são escassos ou a capacidade computacional é limitada. Na segunda linha de investigação foi desenvolvido um modelo de segmentação de instâncias para múltiplas patologias (cancro de bexiga invasivo e não invasivo e inflamações) baseado na rede Mask R-CNN. Esta rede aborda a baixa qualidade das segmentações das lesões reportadas em modelos anteriores através da utilização de mapas de características de mais baixo nível e das informações de fronteira das lesões. Para aumentar a diversidade dos dados foi desenvolvida uma rede condicional pix2pixHD ajustada por textura. O otimizador momentum de 2.ª ordem desenvolvido mostrou ser benéfico relativamente ao momentum clássico. Os métodos desenvolvidos mostraram que podem ser integrados na rotina dos urologistas após algumas melhorias.The influence of medical imaging in supporting diagnosis and treatments in healthcare units is constantly growing. Regarding urinary organs, white light cystoscopy is the standard procedure that allows direct inner visualization and is especially used for lower urinary tract lesions diagnosis, such as bladder cancer, as well as its resection. Despite providing a clear overview of the organ, cystoscopy often fails to accurately diagnose, as it relies on a posterior tissue biopsy confirmation and on the urologists' experience and expertise. In this thesis, the main objective was to develop solutions for the automatic detection and classification of different lesions present in the bladder. These developments included the creation of an annotated dataset of bladder cancers and inflammations and two approaches applying machine learning algorithms. The first research line consisted of a feature-engineering-based classification ensemble, which follows a feature fusion scheme to preserve diversity; the main property o ensembles. This system takes advantage of features of different nature to improve single models’ performances that rely on color and texture characterization (Local binary Patterns, Wavelets, and color-dependent Histogram of Oriented Gradients). Both feature extraction and classification methods performed similarly to those obtained using Deep Learning strategies, such as Transfer Learning and Capsule Neural Networks, and may be preferable when having small datasets or limited computational power. The second research line consisted of a multiple pathology instance segmentation method based on Mask R-CNN in which non-muscle-invasive and muscle-invasive bladder cancer and inflammation could be segmented and classified. This framework addresses the lack of mask quality reported in previous models taking advantage of low-level feature maps and objects’ boundaries information. Data augmentation carried out by a texture-modeled pix2pixHD conditional Generative Adversarial Network expanded data diversity, and the proposed accelerated second-order momentum optimizer significantly improved the training. The developed methods proved that with some improvements, such as by creating a larger well-annotated public database, they could be part of urologists’ daily routine.The author was funded by the grant SFRH/BD/139061/2018 from the Portuguese Foundation for Science and Technology (FCT), with funds from the European Social Fund (FSE), under the Human Capital Operational Programme (POCH) from Portugal 2020 Programme

Zika virus in the Americas: Early epidemiological and genetic findings

Submitted by sandra infurna ([email protected]) on 2016-06-21T16:53:42Z No. of bitstreams: 1 gonzalo2_bello_etal_IOC_2016.pdf: 1066180 bytes, checksum: d43c1cf1b828de79e634ed276cc62178 (MD5)Approved for entry into archive by sandra infurna ([email protected]) on 2016-06-21T17:27:43Z (GMT) No. of bitstreams: 1 gonzalo2_bello_etal_IOC_2016.pdf: 1066180 bytes, checksum: d43c1cf1b828de79e634ed276cc62178 (MD5)Made available in DSpace on 2016-06-21T17:27:43Z (GMT). No. of bitstreams: 1 gonzalo2_bello_etal_IOC_2016.pdf: 1066180 bytes, checksum: d43c1cf1b828de79e634ed276cc62178 (MD5) Previous issue date: 2016Submitted by Angelo Silva ([email protected]) on 2016-07-07T11:16:45Z No. of bitstreams: 3 gonzalo2_bello_etal_IOC_2016.pdf.txt: 51037 bytes, checksum: bebf604bcb5623ddff92fec2bebc02a5 (MD5) gonzalo2_bello_etal_IOC_2016.pdf: 1066180 bytes, checksum: d43c1cf1b828de79e634ed276cc62178 (MD5) license.txt: 2991 bytes, checksum: 5a560609d32a3863062d77ff32785d58 (MD5)Approved for entry into archive by sandra infurna ([email protected]) on 2016-07-07T11:43:23Z (GMT) No. of bitstreams: 3 license.txt: 2991 bytes, checksum: 5a560609d32a3863062d77ff32785d58 (MD5) gonzalo2_bello_etal_IOC_2016.pdf: 1066180 bytes, checksum: d43c1cf1b828de79e634ed276cc62178 (MD5) gonzalo2_bello_etal_IOC_2016.pdf.txt: 51037 bytes, checksum: bebf604bcb5623ddff92fec2bebc02a5 (MD5)Made available in DSpace on 2016-07-07T11:43:23Z (GMT). No. of bitstreams: 3 license.txt: 2991 bytes, checksum: 5a560609d32a3863062d77ff32785d58 (MD5) gonzalo2_bello_etal_IOC_2016.pdf: 1066180 bytes, checksum: d43c1cf1b828de79e634ed276cc62178 (MD5) gonzalo2_bello_etal_IOC_2016.pdf.txt: 51037 bytes, checksum: bebf604bcb5623ddff92fec2bebc02a5 (MD5) Previous issue date: 2016Ministério da Saúde. Instituto Evandro Chagas, Centro de Inovação tecnológica. Ananindeua, PA, Brasil / University of Oxford. Department of Zoology. Oxford, UK.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.University of Oxford. Department of Zoology. Oxford, UK.Universidade de São Paulo. Instituto Adolfo Lutz. São Paulo, SP, Brasil.Universidade de São Paulo. Instituto Adolfo Lutz. São Paulo, SP, Brasil.University of Oxford. Department of Zoology. Oxford, UK.University of Oxford. Department of Zoology. Oxford, UK.University of Oxford. Department of Zoology. Oxford, UK.University of Oxford. Wellcome Trust Centre for Human Genetics. Oxford, UK.University of Oxford. Wellcome Trust Centre for Human Genetics. Oxford, UK.Universidade de São Paulo. Instituto Adolfo Lutz. São Paulo, SP, Brasil.Universidade de São Paulo. Instituto Adolfo Lutz. São Paulo, SP, Brasil.Universidade de São Paulo. Instituto Adolfo Lutz. São Paulo, SP, Brasil.Universidade de São Paulo. Instituto Adolfo Lutz. São Paulo, SP, Brasil.Universidade de São Paulo. Instituto Adolfo Lutz. São Paulo, SP, Brasil.Universidade de São Paulo. Instituto Adolfo Lutz. São Paulo, SP, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.University of Oxford. Department of Zoology. Oxford, UK / Metabiota. San Francisco, CA 94104, USA.University of Oxford. Department of Zoology. Oxford, UK.University of Oxford. Department of Zoology. Oxford, UK.Fundação Oswaldo Cruz. Salvador, BA, Brasil.Universidade Estadual de Feira de Santana, Feira de Santana. Departamento de Saúde. Centro de Pós-Graduação em Saúde Coletiva. Feira de Santana, BA, Brasil.Fundação Oswaldo Cruz. Salvador, BA, Brasil.University of Washington. Institute for Health Metrics and Evaluation,. Seattle, WA, USA / University of Oxford. Wellcome Trust Centre for Human Genetics. Oxford, UK.Ministério da Saúde. Instituto Evandro Chagas, Centro de Inovação tecnológica. Ananindeua, PA, Brasil.Ministério da Saúde. Instituto Evandro Chagas, Centro de Inovação tecnológica. Ananindeua, PA, BrasilMinistério da Saúde. Instituto Evandro Chagas, Centro de Inovação tecnológica. Ananindeua, PA, BrasilMinistério da Saúde. Instituto Evandro Chagas, Centro de Inovação tecnológica. Ananindeua, PA, BrasilMinistério da Saúde. Instituto Evandro Chagas, Centro de Inovação tecnológica. Ananindeua, PA, BrasilMinistério da Saúde. Instituto Evandro Chagas, Centro de Inovação tecnológica. Ananindeua, PA, BrasilMinistério da Saúde. Instituto Evandro Chagas, Centro de Inovação tecnológica. Ananindeua, PA, BrasilMinistério da Saúde. Instituto Evandro Chagas, Centro de Inovação tecnológica. Ananindeua, PA, BrasilMinistério da Saúde. Instituto Evandro Chagas, Centro de Inovação tecnológica. Ananindeua, PA, BrasilFundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de AIDS e Imunologia Molecular. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de AIDS e Imunologia Molecular. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de AIDS e Imunologia Molecular. Rio de Janeiro, RJ, Brasil.Li Ka Shing Knowledge Institute. St. Michael’s Hospital. Toronto, Canada / University of Toronto. Department of Medicine. Division of Infectious Diseases. Toronto, Canada.University of Toronto.Dalla Lana School of Public Health. Toronto, Canada;Brasil. Ministério da Saúde. Brasília, DF, Brasil.Brasil. Ministério da Saúde. Brasília, DF, Brasil.University of Texas Medical Branch. Department of Pathology. Galveston, TX, USA.University of Oxford. Department of Zoology. Oxford, UK / Metabiota. San Francisco, CA 94104, USA.Ministério da Saúde. Instituto Evandro Chagas, Centro de Inovação tecnológica. Ananindeua, PA, Brasil / University of Texas Medical Branch. Department of Pathology. Galveston, TX, USA.Ministério da Saúde. Instituto Evandro Chagas. Departamento de Arbovirologia e Febres Hemorrágicas. Ananindeua, PA, Brasil.Brazil has experienced an unprecedented epidemic of Zika virus (ZIKV), with ~30,000 cases reported to date. ZIKV was first detected in Brazil in May 2015 and cases of microcephaly potentially associated with ZIKV infection were identified in November 2015. Using next generation sequencing we generated seven Brazilian ZIKV genomes, sampled from four self-limited cases, one blood donor, one fatal adult case, and one newborn with microcephaly and congenital malformations. Phylogenetic and molecular clock analyses show a single introduction of ZIKV into the Americas, estimated to have occurred between May-Dec 2013, more than 12 months prior to the detection of ZIKV in Brazil. The estimated date of origin coincides with an increase in air passengers to Brazil from ZIKV endemic areas, and with reported outbreaks in Pacific Islands. ZIKV genomes from Brazil are phylogenetically interspersed with those from other South American and Caribbean countries. Mapping mutations onto existing structural models revealed the context of viral amino acid changes present in the outbreak lineage; however no shared amino acid changes were found among the three currently available virus genomes from microcephaly cases. Municipality-level incidence data indicate that reports of suspected microcephaly in Brazil best correlate with ZIKV incidence around week 17 of pregnancy, although this does not demonstrate causation. Our genetic description and analysis of ZIKV isolates in Brazil provide a baseline for future studies of the evolution and molecular epidemiology in the Americas of this emerging virus

Data from: Zika virus in the Americas: early epidemiological and genetic findings

Brazil has experienced an unprecedented epidemic of Zika virus (ZIKV), with ~30,000 cases reported to date. ZIKV was first detected in Brazil in May 2015 and cases of microcephaly potentially associated with ZIKV infection were identified in November 2015. Using next generation sequencing we generated seven Brazilian ZIKV genomes, sampled from four self-limited cases, one blood donor, one fatal adult case, and one newborn with microcephaly and congenital malformations. Phylogenetic and molecular clock analyses show a single introduction of ZIKV into the Americas, estimated to have occurred between May-Dec 2013, more than 12 months prior to the detection of ZIKV in Brazil. The estimated date of origin coincides with an increase in air passengers to Brazil from ZIKV endemic areas, and with reported outbreaks in Pacific Islands. ZIKV genomes from Brazil are phylogenetically interspersed with those from other South American and Caribbean countries. Mapping mutations onto existing structural models revealed the context of viral amino acid changes present in the outbreak lineage; however no shared amino acid changes were found among the three currently available virus genomes from microcephaly cases. Municipality-level incidence data indicate that reports of suspected microcephaly in Brazil best correlate with ZIKV incidence around week 17 of pregnancy, although this does not demonstrate causation. Our genetic description and analysis of ZIKV isolates in Brazil provide a baseline for future studies of the evolution and molecular epidemiology in the Americas of this emerging virus

Epidemiological Data: Numbers of suspected ZIKV cases and suspected microcephaly cases per state and per epidemiological week.

Contains 1) CSV file with number suspected ZIKV cases from January 2015 to the end of December 2015; 2) CSV file with number of suspected microcephaly cases from January 2015 to the first week of January 2016. Numbers correspond to suspected microcephaly cases at week 20 of pregnancy; 3) CSV file with codes of state of residence and municipality of residence in Brazil; and 4) R scripts for correlation analysis described in SI Section 1.5

Sequence data details and alignments for dataset A and B.

Contains (1) table with accession numbers, isolate names, cell passage history, publication details, country/location of sampling, sampling dates and (2) Fasta format sequence alignments of datasets A and B

BEAST XML input file used for genetic analysis.

BEAST XML input file used to generate Figure 3 under a strict clock model, a Bayesian skyline coalescent prior and a CTMC prior on the clock rate

Characterisation of microbial attack on archaeological bone

As part of an EU funded project to investigate the factors influencing bone preservation in the archaeological record, more than 250 bones from 41 archaeological sites in five countries spanning four climatic regions were studied for diagenetic alteration. Sites were selected to cover a range of environmental conditions and archaeological contexts. Microscopic and physical (mercury intrusion porosimetry) analyses of these bones revealed that the majority (68%) had suffered microbial attack. Furthermore, significant differences were found between animal and human bone in both the state of preservation and the type of microbial attack present. These differences in preservation might result from differences in early taphonomy of the bones. © 2003 Elsevier Science Ltd. All rights reserved

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)

Global variation in postoperative mortality and complications after cancer surgery: a multicentre, prospective cohort study in 82 countries

© 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 licenseBackground: 80% of individuals with cancer will require a surgical procedure, yet little comparative data exist on early outcomes in low-income and middle-income countries (LMICs). We compared postoperative outcomes in breast, colorectal, and gastric cancer surgery in hospitals worldwide, focusing on the effect of disease stage and complications on postoperative mortality. Methods: This was a multicentre, international prospective cohort study of consecutive adult patients undergoing surgery for primary breast, colorectal, or gastric cancer requiring a skin incision done under general or neuraxial anaesthesia. The primary outcome was death or major complication within 30 days of surgery. Multilevel logistic regression determined relationships within three-level nested models of patients within hospitals and countries. Hospital-level infrastructure effects were explored with three-way mediation analyses. This study was registered with ClinicalTrials.gov, NCT03471494. Findings: Between April 1, 2018, and Jan 31, 2019, we enrolled 15 958 patients from 428 hospitals in 82 countries (high income 9106 patients, 31 countries; upper-middle income 2721 patients, 23 countries; or lower-middle income 4131 patients, 28 countries). Patients in LMICs presented with more advanced disease compared with patients in high-income countries. 30-day mortality was higher for gastric cancer in low-income or lower-middle-income countries (adjusted odds ratio 3·72, 95% CI 1·70–8·16) and for colorectal cancer in low-income or lower-middle-income countries (4·59, 2·39–8·80) and upper-middle-income countries (2·06, 1·11–3·83). No difference in 30-day mortality was seen in breast cancer. The proportion of patients who died after a major complication was greatest in low-income or lower-middle-income countries (6·15, 3·26–11·59) and upper-middle-income countries (3·89, 2·08–7·29). Postoperative death after complications was partly explained by patient factors (60%) and partly by hospital or country (40%). The absence of consistently available postoperative care facilities was associated with seven to 10 more deaths per 100 major complications in LMICs. Cancer stage alone explained little of the early variation in mortality or postoperative complications. Interpretation: Higher levels of mortality after cancer surgery in LMICs was not fully explained by later presentation of disease. The capacity to rescue patients from surgical complications is a tangible opportunity for meaningful intervention. Early death after cancer surgery might be reduced by policies focusing on strengthening perioperative care systems to detect and intervene in common complications. Funding: National Institute for Health Research Global Health Research Unit