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Article

Deep Learning to Measure the Intensity of Indocyanine Green in Endometriosis Surgeries with Intestinal Resection

by
Alicia Hernández
1,2,†,
Pablo Robles de Zulueta
3,†,
Emanuela Spagnolo
1,*,
Cristina Soguero
3,*,
Ignacio Cristobal
1,
Isabel Pascual
4,
Ana López
1 and
David Ramiro-Cortijo
5
1
Department of Obstetrics and Gynecology, Hospital Universitario La Paz, Paseo de la Castellana, 261, 28046 Madrid, Spain
2
Department of Obstetrics and Gynecology, Faculty of Medicine, Universidad Autónoma de Madrid, C/Arzobispo Morcillo 2, 28029 Madrid, Spain
3
Department of Signal Theory and Communications, Telematics and Computing Systems, Universidad Rey Juan Carlos, Camino del Molino, 5, D201, Departamental III, 28942 Fuenlabrada, Spain
4
Department of General Surgery, Hospital Universitario La Paz, Paseo de la Castellana, 261, 28046 Madrid, Spain
5
Department of Physiology, Faculty of Medicine, Universidad Autónoma de Madrid, C/Arzobispo Morcillo 2, 28049 Madrid, Spain
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
J. Pers. Med. 2022, 12(6), 982; https://doi.org/10.3390/jpm12060982
Submission received: 4 May 2022 / Revised: 11 June 2022 / Accepted: 15 June 2022 / Published: 16 June 2022
(This article belongs to the Special Issue Endometriosis: Advances in Diagnosis and Treatment)
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Abstract

Endometriosis is a gynecological pathology that affects between 6 and 15% of women of childbearing age. One of the manifestations is intestinal deep infiltrating endometriosis. This condition may force patients to resort to surgical treatment, often ending in resection. The level of blood perfusion at the anastomosis is crucial for its outcome, for this reason, indocyanine green (ICG), a fluorochrome that green stains the structures where it is present, is injected during surgery. This study proposes a novel method based on deep learning algorithms for quantifying the level of blood perfusion in anastomosis. Firstly, with a deep learning algorithm based on the U-Net, models capable of automatically segmenting the intestine from the surgical videos were generated. Secondly, blood perfusion level, from the already segmented video frames, was quantified. The frames were characterized using textures, precisely nine first- and second-order statistics, and then two experiments were carried out. In the first experiment, the differences in the perfusion between the two-anastomosis parts were determined, and in the second, it was verified that the ICG variation could be captured through the textures. The best model when segmenting has an accuracy of 0.92 and a dice coefficient of 0.96. It is concluded that segmentation of the bowel using the U-Net was successful, and the textures are appropriate descriptors for characterization of the blood perfusion in the images where ICG is present. This might help to predict whether postoperative complications will occur during surgery, enabling clinicians to act on this information.
Keywords: deep endometriosis; deep learning; video protocol; automatic segmentation; bowel resection; laparoscopy deep endometriosis; deep learning; video protocol; automatic segmentation; bowel resection; laparoscopy
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MDPI and ACS Style

Hernández, A.; de Zulueta, P.R.; Spagnolo, E.; Soguero, C.; Cristobal, I.; Pascual, I.; López, A.; Ramiro-Cortijo, D. Deep Learning to Measure the Intensity of Indocyanine Green in Endometriosis Surgeries with Intestinal Resection. J. Pers. Med. 2022, 12, 982. https://doi.org/10.3390/jpm12060982

AMA Style

Hernández A, de Zulueta PR, Spagnolo E, Soguero C, Cristobal I, Pascual I, López A, Ramiro-Cortijo D. Deep Learning to Measure the Intensity of Indocyanine Green in Endometriosis Surgeries with Intestinal Resection. Journal of Personalized Medicine. 2022; 12(6):982. https://doi.org/10.3390/jpm12060982

Chicago/Turabian Style

Hernández, Alicia, Pablo Robles de Zulueta, Emanuela Spagnolo, Cristina Soguero, Ignacio Cristobal, Isabel Pascual, Ana López, and David Ramiro-Cortijo. 2022. "Deep Learning to Measure the Intensity of Indocyanine Green in Endometriosis Surgeries with Intestinal Resection" Journal of Personalized Medicine 12, no. 6: 982. https://doi.org/10.3390/jpm12060982

APA Style

Hernández, A., de Zulueta, P. R., Spagnolo, E., Soguero, C., Cristobal, I., Pascual, I., López, A., & Ramiro-Cortijo, D. (2022). Deep Learning to Measure the Intensity of Indocyanine Green in Endometriosis Surgeries with Intestinal Resection. Journal of Personalized Medicine, 12(6), 982. https://doi.org/10.3390/jpm12060982

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