RT-utils: A Minimal Python Library for RT-struct Manipulation

Towards the need for automated and precise AI-based analysis of medical images, we present RT-utils, a specialized Python library tuned for the manipulation of radiotherapy (RT) structures stored in DICOM format. RT-utils excels in converting the polygon contours into binary masks, ensuring accuracy and efficiency. By converting DICOM RT structures into standardized formats such as NumPy arrays and SimpleITK Images, RT-utils optimizes inputs for computational solutions such as AI-based automated segmentation techniques or radiomics analysis. Since its inception in 2020, RT-utils has been used extensively with a focus on simplifying complex data processing tasks. RT-utils offers researchers a powerful solution to enhance workflows and drive significant advancements in medical imaging

Quince (Cydonia oblonga) in vitro plant root formation through an automated temporary inmersion system, and its acclimation

Artículo científicoQuince (Cydonia oblonga) is a non-traditional fruit tree found in Costa Rica that has therapeutic and nutritional properties; however its slow growth and root formation prevents the production of a homogeneous population when using conventional farming techniques. Hence, the aim of this research project was to generate uniform plant material in a reduced time span using a temporary immersion bioreactor system (RITAS ®). A semisolid rooting MS culture medium supplemented with 0.1 mg L-1 NAA; 0.3 mg L-1 IBA and 3% sucrose (pH 6.5), developed in the Centro de Investigación en Biotecnología (CIB), Instituto Tecnológico de Costa Rica (ITCR), in Cartago, was used as a reference medium. Four different variations in the sucrose concentration (1%, 2%, 3%, and 4%) were performed in liquid medium. Each trial was evaluated with in vitro plants which had been previously exposed to the culture medium of the corresponding treatments, in a stationary mode and for a 15 day long period, and with in vitro plants without any previous treatment (a total of eight treatments). The comparison of the root formation percentages evidenced the clear effect of sucrose concentration used, with the best results obtained when using the 2% sucrose trial with no pre-treatment (73.3%). The in vitro plants were acclimated in cylinders made out of peat, have previously been disinfected with fungicide, and placed in a humidity chamber at a 20.5°C average temperature and a 75,5% relative humidity for the establishment of weekly fertilizing cycles. The acclimation process generated an 80% survival rate, since several seedlings experienced stem strangulation caused by a fungal attack. The conidiophores identified through optical and scanning electron microscopy evidenced the presence of Cladosporium spp., which was controlled with carbendazim and iprodione fungicides

A cascaded deep network for automated tumor detection and segmentation in clinical PET imaging of diffuse large B-cell lymphoma

Accurate detection and segmentation of diffuse large B-cell lymphoma (DLBCL) from PET images has important implications for estimation of total metabolic tumor volume, radiomics analysis, surgical intervention and radiotherapy. Manual segmentation of tumors in whole-body PET images is time-consuming, labor-intensive and operator-dependent. In this work, we develop and validate a fast and efficient three-step cascaded deep learning model for automated detection and segmentation of DLBCL tumors from PET images. As compared to a single end-to-end network for segmentation of tumors in whole-body PET images, our three-step model is more effective (improves 3D Dice score from 58.9% to 78.1%) since each of its specialized modules, namely the slice classifier, the tumor detector and the tumor segmentor, can be trained independently to a high degree of skill to carry out a specific task, rather than a single network with suboptimal performance on overall segmentation.Comment: 8 pages, 3 figures, 3 table

A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset

Automated slice classification is clinically relevant since it can be incorporated into medical image segmentation workflows as a preprocessing step that would flag slices with a higher probability of containing tumors, thereby directing physicians attention to the important slices. In this work, we train a ResNet-18 network to classify axial slices of lymphoma PET/CT images (collected from two institutions) depending on whether the slice intercepted a tumor (positive slice) in the 3D image or if the slice did not (negative slice). Various instances of the network were trained on 2D axial datasets created in different ways: (i) slice-level split and (ii) patient-level split; inputs of different types were used: (i) only PET slices and (ii) concatenated PET and CT slices; and different training strategies were employed: (i) center-aware (CAW) and (ii) center-agnostic (CAG). Model performances were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC), and various binary classification metrics. We observe and describe a performance overestimation in the case of slice-level split as compared to the patient-level split training. The model trained using patient-level split data with the network input containing only PET slices in the CAG training regime was the best performing/generalizing model on a majority of metrics. Our models were additionally more closely compared using the sensitivity metric on the positive slices from their respective test sets.Comment: 10 pages, 6 figures, 2 table

Treatment of laboratory wastes by heterogeneous photocatalysis with TiO2

La contaminación ambiental causada por la generación de desechos peligrosos es un problema creciente y globalizado. Los residuos peligrosos, una vez emitidos, pueden permanecer en el ambiente durante cientos de años. En diversos laboratorios universitarios se trabaja con distintas sustancias químicas y se efectúan una serie de operaciones que conllevan a la generación de residuos que, en la mayoría de los casos, son peligrosos para la salud y el ambiente; dentro de estos residuos están los generados en los procedimientos de la coloración de Gram.En este trabajo, se presenta una alternativa para el tratamiento de los residuos de la tinción de Gram vía Fotocatálisis Heterogénea (FH). Se utilizó TiO2 P-25 de la casa comer-cial Evonik® y como fuente de radiación una lámpara germicida; además, se usaron como técnicas de análisis la espectroscopia UV – Vis acompañada de mediciones de DQO.Dada la naturaleza de estos residuos, se determinó tratarlos en concentraciones menores a las reales (diluidas al 10 %), con dosificaciones de TiO2 según la literatura en procesos con colorantes; alcanzando en 2 horas una degradación alrededor del 70 % y una reducción de la DQO del 40 %, mostrando la viabilidad de la posible implementación de este proceso en su eliminación.Environmental pollution caused by hazardous waste is a growing and globalized prob-lem. Such waste, once emitted, can remain in the environment for hundreds of years. Chem-ical substances are handled and several operations are carried out in different university laboratories, which generates waste that in most cases is dangerous to human health and the environment. Some of these residues include those produced during Gram staining pro-cedures.This paper presents an alternative for treating residues of Gram staining by Heteroge-neous Photocatalysis (HP). Evonik® TiO2 P-25 was used, and the radiation source was a germicidal lamp. In addition, UV-Vis spectroscopy together with COD measurements were used as analytical techniques.In view of the nature of these residues, it was decided to treat them in concentrations lower than real ones (diluted to 10 %), with TiO2 dosages according to the literature on dy-ing processes. Within 2 hours, a degradation of around 70 % and a reduction of 40 % of the COD were achieved, which shows the feasibility of the implementation of this process to eliminate said wastes

Enfermedades crónicas

Adherencia al tratamiento farmacológico y relación con el control metabólico en pacientes con DM2Aluminio en pacientes con terapia de reemplazo renal crónico con hemodiálisis en Bogotá, ColombiaAmputación de extremidades inferiores: ¿están aumentando las tasas?Consumo de edulcorantes artificiales en jóvenes universitariosCómo crecen niños normales de 2 años que son sobrepeso a los 7 añosDiagnóstico con enfoque territorial de salud cardiovascular en la Región MetropolitanaEfecto a corto plazo de una intervención con ejercicio físico, en niños con sobrepesoEfectos de la cirugía bariátrica en pacientes con síndrome metabólico e IMC < 35 KG/M2Encuesta mundial de tabaquismo en estudiantes de profesiones de saludEnfermedades crónicas no transmisibles: Consecuencias sociales-sanitarias de comunidades rurales en ChileEpidemiología de las muertes hospitalarias por patologías relacionadas a muerte encefálica, Chile 2003-2007Estado nutricional y conductas alimentarias en adolescentes de 4º medio de la Región de CoquimboEstudio de calidad de vida en una muestra del plan piloto para hepatitis CEvaluación del proceso asistencial y de resultados de salud del GES de diabetes mellitus 2Factores de riesgo cardiovascular en población universitaria de la Facsal, universidad de TarapacáImplicancias psicosociales en la génesis, evolución y tratamiento de pacientes con hipertensión arterial esencialInfarto agudo al miocardio (IAM): Realidad en el Hospital de Puerto Natales, 2009-2010Introducción de nuevas TIC y mejoría de la asistencia a un programa de saludNiños obesos atendidos en el Cesfam de Puerto Natales y su entorno familiarPerfil de la mortalidad por cáncer de cuello uterino en Río de JaneiroPerfil del paciente primo-consultante del Programa de Salud Cardiovascular, Consultorio Cordillera Andina, Los AndesPrevalencia de automedicación en mujeres beneficiarias del Hospital Comunitario de Til-TiPrevalencia de caries en población preescolar y su relación con malnutrición por excesoPrevalencia de retinopatía diabética en comunas dependientes del Servicio de Salud Metropolitano Occidente (SSMOC)Problemas de adherencia farmacológica antihipertensiva en población mapuche: Un estudio cualitativoRol biológico de los antioxidantes innatos en pacientes portadores de VIH/SidaSobrepeso en empleados de un restaurante de una universidad pública del estado de São Paul

Measurements of the pp → ZZ production cross section and the Z → 4ℓ branching fraction, and constraints on anomalous triple gauge couplings at √s = 13 TeV

Four-lepton production in proton-proton collisions, pp -> (Z/gamma*)(Z/gamma*) -> 4l, where l = e or mu, is studied at a center-of-mass energy of 13 TeV with the CMS detector at the LHC. The data sample corresponds to an integrated luminosity of 35.9 fb(-1). The ZZ production cross section, sigma(pp -> ZZ) = 17.2 +/- 0.5 (stat) +/- 0.7 (syst) +/- 0.4 (theo) +/- 0.4 (lumi) pb, measured using events with two opposite-sign, same-flavor lepton pairs produced in the mass region 60 4l) = 4.83(-0.22)(+0.23) (stat)(-0.29)(+0.32) (syst) +/- 0.08 (theo) +/- 0.12(lumi) x 10(-6) for events with a four-lepton invariant mass in the range 80 4GeV for all opposite-sign, same-flavor lepton pairs. The results agree with standard model predictions. The invariant mass distribution of the four-lepton system is used to set limits on anomalous ZZZ and ZZ. couplings at 95% confidence level: -0.0012 < f(4)(Z) < 0.0010, -0.0010 < f(5)(Z) < 0.0013, -0.0012 < f(4)(gamma) < 0.0013, -0.0012 < f(5)(gamma) < 0.0013

Semi-supervised learning towards automated segmentation of PET images with limited annotations:application to lymphoma patients

Manual segmentation poses a time-consuming challenge for disease quantification, therapy evaluation, treatment planning, and outcome prediction. Convolutional neural networks (CNNs) hold promise in accurately identifying tumor locations and boundaries in PET scans. However, a major hurdle is the extensive amount of supervised and annotated data necessary for training. To overcome this limitation, this study explores semi-supervised approaches utilizing unlabeled data, specifically focusing on PET images of diffuse large B-cell lymphoma (DLBCL) and primary mediastinal large B-cell lymphoma (PMBCL) obtained from two centers. We considered 2-[18F]FDG PET images of 292 patients PMBCL (n = 104) and DLBCL (n = 188) (n = 232 for training and validation, and n = 60 for external testing). We harnessed classical wisdom embedded in traditional segmentation methods, such as the fuzzy clustering loss function (FCM), to tailor the training strategy for a 3D U-Net model, incorporating both supervised and unsupervised learning approaches. Various supervision levels were explored, including fully supervised methods with labeled FCM and unified focal/Dice loss, unsupervised methods with robust FCM (RFCM) and Mumford-Shah (MS) loss, and semi-supervised methods combining FCM with supervised Dice loss (MS + Dice) or labeled FCM (RFCM + FCM). The unified loss function yielded higher Dice scores (0.73 ± 0.11; 95% CI 0.67–0.8) than Dice loss (p value &lt; 0.01). Among the semi-supervised approaches, RFCM + αFCM (α = 0.3) showed the best performance, with Dice score of 0.68 ± 0.10 (95% CI 0.45–0.77), outperforming MS + αDice for any supervision level (any α) (p &lt; 0.01). Another semi-supervised approach with MS + αDice (α = 0.2) achieved Dice score of 0.59 ± 0.09 (95% CI 0.44–0.76) surpassing other supervision levels (p &lt; 0.01). Given the time-consuming nature of manual delineations and the inconsistencies they may introduce, semi-supervised approaches hold promise for automating medical imaging segmentation workflows.</p

Semi-supervised learning towards automated segmentation of PET images with limited annotations: Application to lymphoma patients

The time-consuming task of manual segmentation challenges routine systematic quantification of disease burden. Convolutional neural networks (CNNs) hold significant promise to reliably identify locations and boundaries of tumors from PET scans. We aimed to leverage the need for annotated data via semi-supervised approaches, with application to PET images of diffuse large B-cell lymphoma (DLBCL) and primary mediastinal large B-cell lymphoma (PMBCL). We analyzed 18F-FDG PET images of 292 patients with PMBCL (n=104) and DLBCL (n=188) (n=232 for training and validation, and n=60 for external testing). We employed FCM and MS losses for training a 3D U-Net with different levels of supervision: i) fully supervised methods with labeled FCM (LFCM) as well as Unified focal and Dice loss functions, ii) unsupervised methods with Robust FCM (RFCM) and Mumford-Shah (MS) loss functions, and iii) Semi-supervised methods based on FCM (RFCM+LFCM), as well as MS loss in combination with supervised Dice loss (MS+Dice). Unified loss function yielded higher Dice score (mean +/- standard deviation (SD)) (0.73 +/- 0.03; 95% CI, 0.67-0.8) compared to Dice loss (p-value<0.01). Semi-supervised (RFCM+alpha*LFCM) with alpha=0.3 showed the best performance, with a Dice score of 0.69 +/- 0.03 (95% CI, 0.45-0.77) outperforming (MS+alpha*Dice) for any supervision level (any alpha) (p<0.01). The best performer among (MS+alpha*Dice) semi-supervised approaches with alpha=0.2 showed a Dice score of 0.60 +/- 0.08 (95% CI, 0.44-0.76) compared to another supervision level in this semi-supervised approach (p<0.01). Semi-supervised learning via FCM loss (RFCM+alpha*LFCM) showed improved performance compared to supervised approaches. Considering the time-consuming nature of expert manual delineations and intra-observer variabilities, semi-supervised approaches have significant potential for automated segmentation workflows