Object detection and statistical analysis of microscopy image sequences

"Confocal microscope images are wide useful in medical diagnosis and research. The automatic interpretation of this type of images is very important but it is a challenging endeavor in image processing area, since these images are heavily contaminated with noise, have low contrast and low resol...

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Detalles Bibliográficos
Autores principales: Hurovitz, Sasha Ivan, Chan, Debora, Ramele, Rodrigo, Gambini, Juliana
Formato: Artículos de Publicaciones Periódicas publishedVersion
Lenguaje:Inglés
Publicado: 2022
Materias:
SEGMENTACION DE IMAGENES
RETINOBLASTOMA
APRENDIZAJE AUTOMATICO
Acceso en línea:https://ri.itba.edu.ar/handle/123456789/3967
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-123456789-3967
record_format dspace
spelling I32-R138-123456789-39672022-12-07T13:05:49Z Object detection and statistical analysis of microscopy image sequences Hurovitz, Sasha Ivan Chan, Debora Ramele, Rodrigo Gambini, Juliana SEGMENTACION DE IMAGENES RETINOBLASTOMA APRENDIZAJE AUTOMATICO "Confocal microscope images are wide useful in medical diagnosis and research. The automatic interpretation of this type of images is very important but it is a challenging endeavor in image processing area, since these images are heavily contaminated with noise, have low contrast and low resolution. This work deals with the problem of analyzing the penetration velocity of a chemotherapy drug in an ocular tumor called retinoblastoma. The primary retinoblastoma cells cultures are exposed to topotecan drug and the penetration evolution is documented by producing sequences of microscopy images. It is possible to quantify the penetration rate of topotecan drug because it produces fluorescence emission by laser excitation which is captured by the camera. In order to estimate the topotecan penetration time in the whole retinoblastoma cell culture, a procedure based on an active contour detection algorithm, a neural network classifier and a statistical model and its validation, is proposed. This new inference model allows to estimate the penetration time. Results show that the penetration mean time strongly depends on tumorsphere size and on chemotherapeutic treatment that the patient has previously received." 2022-11-04T18:47:09Z 2022-11-04T18:47:09Z 2022 Artículos de Publicaciones Periódicas info:eu-repo/semantics/publishedVersion 1577-5097 https://ri.itba.edu.ar/handle/123456789/3967 en info:eu-repo/semantics/altIdentifier/doi/10.5565/rev/elcvia.1482 http://creativecommons.org/licenses/by-nc-sa/X.0/ application/pdf application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic SEGMENTACION DE IMAGENES
RETINOBLASTOMA
APRENDIZAJE AUTOMATICO
spellingShingle SEGMENTACION DE IMAGENES
RETINOBLASTOMA
APRENDIZAJE AUTOMATICO
Hurovitz, Sasha Ivan
Chan, Debora
Ramele, Rodrigo
Gambini, Juliana
Object detection and statistical analysis of microscopy image sequences
topic_facet SEGMENTACION DE IMAGENES
RETINOBLASTOMA
APRENDIZAJE AUTOMATICO
description "Confocal microscope images are wide useful in medical diagnosis and research. The automatic interpretation of this type of images is very important but it is a challenging endeavor in image processing area, since these images are heavily contaminated with noise, have low contrast and low resolution. This work deals with the problem of analyzing the penetration velocity of a chemotherapy drug in an ocular tumor called retinoblastoma. The primary retinoblastoma cells cultures are exposed to topotecan drug and the penetration evolution is documented by producing sequences of microscopy images. It is possible to quantify the penetration rate of topotecan drug because it produces fluorescence emission by laser excitation which is captured by the camera. In order to estimate the topotecan penetration time in the whole retinoblastoma cell culture, a procedure based on an active contour detection algorithm, a neural network classifier and a statistical model and its validation, is proposed. This new inference model allows to estimate the penetration time. Results show that the penetration mean time strongly depends on tumorsphere size and on chemotherapeutic treatment that the patient has previously received."
format Artículos de Publicaciones Periódicas
publishedVersion
author Hurovitz, Sasha Ivan
Chan, Debora
Ramele, Rodrigo
Gambini, Juliana
author_facet Hurovitz, Sasha Ivan
Chan, Debora
Ramele, Rodrigo
Gambini, Juliana
author_sort Hurovitz, Sasha Ivan
title Object detection and statistical analysis of microscopy image sequences
title_short Object detection and statistical analysis of microscopy image sequences
title_full Object detection and statistical analysis of microscopy image sequences
title_fullStr Object detection and statistical analysis of microscopy image sequences
title_full_unstemmed Object detection and statistical analysis of microscopy image sequences
title_sort object detection and statistical analysis of microscopy image sequences
publishDate 2022
url https://ri.itba.edu.ar/handle/123456789/3967
work_keys_str_mv AT hurovitzsashaivan objectdetectionandstatisticalanalysisofmicroscopyimagesequences
AT chandebora objectdetectionandstatisticalanalysisofmicroscopyimagesequences
AT ramelerodrigo objectdetectionandstatisticalanalysisofmicroscopyimagesequences
AT gambinijuliana objectdetectionandstatisticalanalysisofmicroscopyimagesequences
_version_ 1765660931265658880

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