Cargando…

Deep Learning Algorithms in the Automatic Segmentation of Liver Lesions in Ultrasound Investigations

Background: The ultrasound is one of the most used medical imaging investigations worldwide. It is non-invasive and effective in assessing liver tumors or other types of parenchymal changes. Methods: The aim of the study was to build a deep learning model for image segmentation in ultrasound video i...

Descripción completa

Detalles Bibliográficos
Autores principales: Mămuleanu, Mădălin, Urhuț, Cristiana Marinela, Săndulescu, Larisa Daniela, Kamal, Constantin, Pătrașcu, Ana-Maria, Ionescu, Alin Gabriel, Șerbănescu, Mircea-Sebastian, Streba, Costin Teodor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9695234/
https://www.ncbi.nlm.nih.gov/pubmed/36431012
http://dx.doi.org/10.3390/life12111877
_version_ 1784838005311143936
author Mămuleanu, Mădălin
Urhuț, Cristiana Marinela
Săndulescu, Larisa Daniela
Kamal, Constantin
Pătrașcu, Ana-Maria
Ionescu, Alin Gabriel
Șerbănescu, Mircea-Sebastian
Streba, Costin Teodor
author_facet Mămuleanu, Mădălin
Urhuț, Cristiana Marinela
Săndulescu, Larisa Daniela
Kamal, Constantin
Pătrașcu, Ana-Maria
Ionescu, Alin Gabriel
Șerbănescu, Mircea-Sebastian
Streba, Costin Teodor
author_sort Mămuleanu, Mădălin
collection PubMed
description Background: The ultrasound is one of the most used medical imaging investigations worldwide. It is non-invasive and effective in assessing liver tumors or other types of parenchymal changes. Methods: The aim of the study was to build a deep learning model for image segmentation in ultrasound video investigations. The dataset used in the study was provided by the University of Medicine and Pharmacy Craiova, Romania and contained 50 video examinations from 49 patients. The mean age of the patients in the cohort was 69.57. Regarding presence of a subjacent liver disease, 36.73% had liver cirrhosis and 16.32% had chronic viral hepatitis (5 patients: chronic hepatitis C and 3 patients: chronic hepatitis B). Frames were extracted and cropped from each examination and an expert gastroenterologist labelled the lesions in each frame. After labelling, the labels were exported as binary images. A deep learning segmentation model (U-Net) was trained with focal Tversky loss as a loss function. Two models were obtained with two different sets of parameters for the loss function. The performance metrics observed were intersection over union and recall and precision. Results: Analyzing the intersection over union metric, the first segmentation model obtained performed better compared to the second model: 0.8392 (model 1) vs. 0.7990 (model 2). The inference time for both models was between 32.15 milliseconds and 77.59 milliseconds. Conclusions: Two segmentation models were obtained in the study. The models performed similarly during training and validation. However, one model was trained to focus on hard-to-predict labels. The proposed segmentation models can represent a first step in automatically extracting time-intensity curves from CEUS examinations.
format Online
Article
Text
id pubmed-9695234
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-96952342022-11-26 Deep Learning Algorithms in the Automatic Segmentation of Liver Lesions in Ultrasound Investigations Mămuleanu, Mădălin Urhuț, Cristiana Marinela Săndulescu, Larisa Daniela Kamal, Constantin Pătrașcu, Ana-Maria Ionescu, Alin Gabriel Șerbănescu, Mircea-Sebastian Streba, Costin Teodor Life (Basel) Article Background: The ultrasound is one of the most used medical imaging investigations worldwide. It is non-invasive and effective in assessing liver tumors or other types of parenchymal changes. Methods: The aim of the study was to build a deep learning model for image segmentation in ultrasound video investigations. The dataset used in the study was provided by the University of Medicine and Pharmacy Craiova, Romania and contained 50 video examinations from 49 patients. The mean age of the patients in the cohort was 69.57. Regarding presence of a subjacent liver disease, 36.73% had liver cirrhosis and 16.32% had chronic viral hepatitis (5 patients: chronic hepatitis C and 3 patients: chronic hepatitis B). Frames were extracted and cropped from each examination and an expert gastroenterologist labelled the lesions in each frame. After labelling, the labels were exported as binary images. A deep learning segmentation model (U-Net) was trained with focal Tversky loss as a loss function. Two models were obtained with two different sets of parameters for the loss function. The performance metrics observed were intersection over union and recall and precision. Results: Analyzing the intersection over union metric, the first segmentation model obtained performed better compared to the second model: 0.8392 (model 1) vs. 0.7990 (model 2). The inference time for both models was between 32.15 milliseconds and 77.59 milliseconds. Conclusions: Two segmentation models were obtained in the study. The models performed similarly during training and validation. However, one model was trained to focus on hard-to-predict labels. The proposed segmentation models can represent a first step in automatically extracting time-intensity curves from CEUS examinations. MDPI 2022-11-14 /pmc/articles/PMC9695234/ /pubmed/36431012 http://dx.doi.org/10.3390/life12111877 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mămuleanu, Mădălin
Urhuț, Cristiana Marinela
Săndulescu, Larisa Daniela
Kamal, Constantin
Pătrașcu, Ana-Maria
Ionescu, Alin Gabriel
Șerbănescu, Mircea-Sebastian
Streba, Costin Teodor
Deep Learning Algorithms in the Automatic Segmentation of Liver Lesions in Ultrasound Investigations
title Deep Learning Algorithms in the Automatic Segmentation of Liver Lesions in Ultrasound Investigations
title_full Deep Learning Algorithms in the Automatic Segmentation of Liver Lesions in Ultrasound Investigations
title_fullStr Deep Learning Algorithms in the Automatic Segmentation of Liver Lesions in Ultrasound Investigations
title_full_unstemmed Deep Learning Algorithms in the Automatic Segmentation of Liver Lesions in Ultrasound Investigations
title_short Deep Learning Algorithms in the Automatic Segmentation of Liver Lesions in Ultrasound Investigations
title_sort deep learning algorithms in the automatic segmentation of liver lesions in ultrasound investigations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9695234/
https://www.ncbi.nlm.nih.gov/pubmed/36431012
http://dx.doi.org/10.3390/life12111877
work_keys_str_mv AT mamuleanumadalin deeplearningalgorithmsintheautomaticsegmentationofliverlesionsinultrasoundinvestigations
AT urhutcristianamarinela deeplearningalgorithmsintheautomaticsegmentationofliverlesionsinultrasoundinvestigations
AT sandulescularisadaniela deeplearningalgorithmsintheautomaticsegmentationofliverlesionsinultrasoundinvestigations
AT kamalconstantin deeplearningalgorithmsintheautomaticsegmentationofliverlesionsinultrasoundinvestigations
AT patrascuanamaria deeplearningalgorithmsintheautomaticsegmentationofliverlesionsinultrasoundinvestigations
AT ionescualingabriel deeplearningalgorithmsintheautomaticsegmentationofliverlesionsinultrasoundinvestigations
AT serbanescumirceasebastian deeplearningalgorithmsintheautomaticsegmentationofliverlesionsinultrasoundinvestigations
AT strebacostinteodor deeplearningalgorithmsintheautomaticsegmentationofliverlesionsinultrasoundinvestigations