Fast unsupervised nuclear segmentation and classification scheme for automatic allred cancer scoring in immunohistochemical breast tissue images.
Identifieur interne : 000496 ( Main/Exploration ); précédent : 000495; suivant : 000497Fast unsupervised nuclear segmentation and classification scheme for automatic allred cancer scoring in immunohistochemical breast tissue images.
Auteurs : Aymen Mouelhi [Tunisie] ; Hana Rmili [Tunisie] ; Jaouher Ben Ali [France] ; Mounir Sayadi [Tunisie] ; Raoudha Doghri [Tunisie] ; Karima Mrad [Tunisie]Source :
- Computer methods and programs in biomedicine [ 1872-7565 ] ; 2018.
Descripteurs français
- KwdFr :
- Algorithmes (MeSH), Apprentissage machine non supervisé (MeSH), Carcinome canalaire du sein (classification), Carcinome canalaire du sein (imagerie diagnostique), Carcinome canalaire du sein (métabolisme), Coloration et marquage (MeSH), Femelle (MeSH), Humains (MeSH), Immunohistochimie (méthodes), Immunohistochimie (statistiques et données numériques), Interprétation d'images assistée par ordinateur (méthodes), Interprétation d'images assistée par ordinateur (statistiques et données numériques), Noyau de la cellule (anatomopathologie), Noyau de la cellule (classification), Noyau de la cellule (métabolisme), Récepteurs des oestrogènes (métabolisme), Récepteurs à la progestérone (métabolisme), Tumeurs du sein (classification), Tumeurs du sein (imagerie diagnostique), Tumeurs du sein (métabolisme).
- MESH :
- anatomopathologie : Noyau de la cellule.
- imagerie diagnostique : Carcinome canalaire du sein, Tumeurs du sein.
- métabolisme : Carcinome canalaire du sein, Noyau de la cellule, Récepteurs des oestrogènes, Récepteurs à la progestérone, Tumeurs du sein.
- méthodes : Immunohistochimie, Interprétation d'images assistée par ordinateur.
- statistiques et données numériques : Immunohistochimie, Interprétation d'images assistée par ordinateur.
- Algorithmes, Apprentissage machine non supervisé, Coloration et marquage, Femelle, Humains.
English descriptors
- KwdEn :
- Algorithms (MeSH), Breast Neoplasms (classification), Breast Neoplasms (diagnostic imaging), Breast Neoplasms (metabolism), Carcinoma, Ductal, Breast (classification), Carcinoma, Ductal, Breast (diagnostic imaging), Carcinoma, Ductal, Breast (metabolism), Cell Nucleus (classification), Cell Nucleus (metabolism), Cell Nucleus (pathology), Female (MeSH), Humans (MeSH), Image Interpretation, Computer-Assisted (methods), Image Interpretation, Computer-Assisted (statistics & numerical data), Immunohistochemistry (methods), Immunohistochemistry (statistics & numerical data), Receptors, Estrogen (metabolism), Receptors, Progesterone (metabolism), Staining and Labeling (MeSH), Unsupervised Machine Learning (MeSH).
- MESH :
- chemical , metabolism : Receptors, Estrogen, Receptors, Progesterone.
- classification : Breast Neoplasms, Carcinoma, Ductal, Breast, Cell Nucleus.
- diagnostic imaging : Breast Neoplasms, Carcinoma, Ductal, Breast.
- metabolism : Breast Neoplasms, Carcinoma, Ductal, Breast, Cell Nucleus.
- methods : Image Interpretation, Computer-Assisted, Immunohistochemistry.
- pathology : Cell Nucleus.
- statistics & numerical data : Image Interpretation, Computer-Assisted, Immunohistochemistry.
- Algorithms, Female, Humans, Staining and Labeling, Unsupervised Machine Learning.
Abstract
BACKGROUND AND OBJECTIVE
This paper presents an improved scheme able to perform accurate segmentation and classification of cancer nuclei in immunohistochemical (IHC) breast tissue images in order to provide quantitative evaluation of estrogen or progesterone (ER/PR) receptor status that will assist pathologists in cancer diagnostic process.
METHODS
The proposed segmentation method is based on adaptive local thresholding and an enhanced morphological procedure, which are applied to extract all stained nuclei regions and to split overlapping nuclei. In fact, a new segmentation approach is presented here for cell nuclei detection from the IHC image using a modified Laplacian filter and an improved watershed algorithm. Stromal cells are then removed from the segmented image using an adaptive criterion in order to get fast tumor nuclei recognition. Finally, unsupervised classification of cancer nuclei is obtained by the combination of four common color separation techniques for a subsequent Allred cancer scoring.
RESULTS
Experimental results on various IHC tissue images of different cancer affected patients, demonstrate the effectiveness of the proposed scheme when compared to the manual scoring of pathological experts. A statistical analysis is performed on the whole image database between immuno-score of manual and automatic method, and compared with the scores that have reached using other state-of-art segmentation and classification strategies. According to the performance evaluation, we recorded more than 98% for both accuracy of detected nuclei and image cancer scoring over the truths provided by experienced pathologists which shows the best correlation with the expert's score (Pearson's correlation coefficient = 0.993, p-value < 0.005) and the lowest computational total time of 72.3 s/image (±1.9) compared to recent studied methods.
CONCLUSIONS
The proposed scheme can be easily applied for any histopathological diagnostic process that needs stained nuclear quantification and cancer grading. Moreover, the reduced processing time and manual interactions of our procedure can facilitate its implementation in a real-time device to construct a fully online evaluation system of IHC tissue images.
DOI: 10.1016/j.cmpb.2018.08.005
PubMed: 30337080
Affiliations:
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Le document en format XML
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Electronic address: rmilihana@gmail.com.</nlm:affiliation><country xml:lang="fr">Tunisie</country><wicri:regionArea>University of Tunis El-Manar, ISTMT, Laboratory of Biophysics and Medical Technologies</wicri:regionArea><wicri:noRegion>Laboratory of Biophysics and Medical Technologies</wicri:noRegion></affiliation></author><author><name sortKey="Ali, Jaouher Ben" sort="Ali, Jaouher Ben" uniqKey="Ali J" first="Jaouher Ben" last="Ali">Jaouher Ben Ali</name><affiliation wicri:level="3"><nlm:affiliation>University of Tunis, ENSIT, LR13ES03 SIME, Montfleury 1008, Tunisia; FEMTO-ST Institute, AS2M department, UMR CNRS 6174 - UFC / ENSMM /UTBM, Besançon 25000, France. Electronic address: benalijaouher@yahoo.fr.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>University of Tunis, ENSIT, LR13ES03 SIME, Montfleury 1008, Tunisia; FEMTO-ST Institute, AS2M department, UMR CNRS 6174 - UFC / ENSMM /UTBM, Besançon 25000</wicri:regionArea><placeName><region type="region" nuts="2">Bourgogne-Franche-Comté</region><region type="old region" nuts="2">Franche-Comté</region></placeName></affiliation></author><author><name sortKey="Sayadi, Mounir" sort="Sayadi, Mounir" uniqKey="Sayadi M" first="Mounir" last="Sayadi">Mounir Sayadi</name><affiliation wicri:level="1"><nlm:affiliation>University of Tunis, ENSIT, LR13ES03 SIME, Montfleury 1008, Tunisia. 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Electronic address: raoudha.doghri@gmail.com.</nlm:affiliation><country xml:lang="fr">Tunisie</country><wicri:regionArea>Salah Azaiez Institute of Oncology, Morbid Anatomy Service, bd du 9 avril, Bab Saadoun, Tunis 1006</wicri:regionArea><placeName><settlement type="city">Tunis</settlement><region nuts="2">Gouvernorat de Tunis</region></placeName></affiliation></author><author><name sortKey="Mrad, Karima" sort="Mrad, Karima" uniqKey="Mrad K" first="Karima" last="Mrad">Karima Mrad</name><affiliation wicri:level="1"><nlm:affiliation>Salah Azaiez Institute of Oncology, Morbid Anatomy Service, bd du 9 avril, Bab Saadoun, Tunis 1006, Tunisia. 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Electronic address: aymen_mouelhi@yahoo.fr.</nlm:affiliation><country xml:lang="fr">Tunisie</country><wicri:regionArea>University of Tunis, ENSIT, LR13ES03 SIME, Montfleury 1008</wicri:regionArea><wicri:noRegion>Montfleury 1008</wicri:noRegion></affiliation></author><author><name sortKey="Rmili, Hana" sort="Rmili, Hana" uniqKey="Rmili H" first="Hana" last="Rmili">Hana Rmili</name><affiliation wicri:level="1"><nlm:affiliation>University of Tunis El-Manar, ISTMT, Laboratory of Biophysics and Medical Technologies, Tunisia. Electronic address: rmilihana@gmail.com.</nlm:affiliation><country xml:lang="fr">Tunisie</country><wicri:regionArea>University of Tunis El-Manar, ISTMT, Laboratory of Biophysics and Medical Technologies</wicri:regionArea><wicri:noRegion>Laboratory of Biophysics and Medical Technologies</wicri:noRegion></affiliation></author><author><name sortKey="Ali, Jaouher Ben" sort="Ali, Jaouher Ben" uniqKey="Ali J" first="Jaouher Ben" last="Ali">Jaouher Ben Ali</name><affiliation wicri:level="3"><nlm:affiliation>University of Tunis, ENSIT, LR13ES03 SIME, Montfleury 1008, Tunisia; FEMTO-ST Institute, AS2M department, UMR CNRS 6174 - UFC / ENSMM /UTBM, Besançon 25000, France. Electronic address: benalijaouher@yahoo.fr.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>University of Tunis, ENSIT, LR13ES03 SIME, Montfleury 1008, Tunisia; FEMTO-ST Institute, AS2M department, UMR CNRS 6174 - UFC / ENSMM /UTBM, Besançon 25000</wicri:regionArea><placeName><region type="region" nuts="2">Bourgogne-Franche-Comté</region><region type="old region" nuts="2">Franche-Comté</region></placeName></affiliation></author><author><name sortKey="Sayadi, Mounir" sort="Sayadi, Mounir" uniqKey="Sayadi M" first="Mounir" last="Sayadi">Mounir Sayadi</name><affiliation wicri:level="1"><nlm:affiliation>University of Tunis, ENSIT, LR13ES03 SIME, Montfleury 1008, Tunisia. Electronic address: mounir.sayadi@esstt.rnu.tn.</nlm:affiliation><country xml:lang="fr">Tunisie</country><wicri:regionArea>University of Tunis, ENSIT, LR13ES03 SIME, Montfleury 1008</wicri:regionArea><wicri:noRegion>Montfleury 1008</wicri:noRegion></affiliation></author><author><name sortKey="Doghri, Raoudha" sort="Doghri, Raoudha" uniqKey="Doghri R" first="Raoudha" last="Doghri">Raoudha Doghri</name><affiliation wicri:level="1"><nlm:affiliation>Salah Azaiez Institute of Oncology, Morbid Anatomy Service, bd du 9 avril, Bab Saadoun, Tunis 1006, Tunisia. Electronic address: raoudha.doghri@gmail.com.</nlm:affiliation><country xml:lang="fr">Tunisie</country><wicri:regionArea>Salah Azaiez Institute of Oncology, Morbid Anatomy Service, bd du 9 avril, Bab Saadoun, Tunis 1006</wicri:regionArea><placeName><settlement type="city">Tunis</settlement><region nuts="2">Gouvernorat de Tunis</region></placeName></affiliation></author><author><name sortKey="Mrad, Karima" sort="Mrad, Karima" uniqKey="Mrad K" first="Karima" last="Mrad">Karima Mrad</name><affiliation wicri:level="1"><nlm:affiliation>Salah Azaiez Institute of Oncology, Morbid Anatomy Service, bd du 9 avril, Bab Saadoun, Tunis 1006, Tunisia. Electronic address: Karima.mrad@rns.tn.</nlm:affiliation><country xml:lang="fr">Tunisie</country><wicri:regionArea>Salah Azaiez Institute of Oncology, Morbid Anatomy Service, bd du 9 avril, Bab Saadoun, Tunis 1006</wicri:regionArea><placeName><settlement type="city">Tunis</settlement><region nuts="2">Gouvernorat de Tunis</region></placeName></affiliation></author></analytic><series><title level="j">Computer methods and programs in biomedicine</title><idno type="eISSN">1872-7565</idno><imprint><date when="2018" type="published">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms (MeSH)</term><term>Breast Neoplasms (classification)</term><term>Breast Neoplasms (diagnostic imaging)</term><term>Breast Neoplasms (metabolism)</term><term>Carcinoma, Ductal, Breast (classification)</term><term>Carcinoma, Ductal, Breast (diagnostic imaging)</term><term>Carcinoma, Ductal, Breast (metabolism)</term><term>Cell Nucleus (classification)</term><term>Cell Nucleus (metabolism)</term><term>Cell Nucleus (pathology)</term><term>Female (MeSH)</term><term>Humans (MeSH)</term><term>Image Interpretation, Computer-Assisted (methods)</term><term>Image Interpretation, Computer-Assisted (statistics & numerical data)</term><term>Immunohistochemistry (methods)</term><term>Immunohistochemistry (statistics & numerical data)</term><term>Receptors, Estrogen (metabolism)</term><term>Receptors, Progesterone (metabolism)</term><term>Staining and Labeling (MeSH)</term><term>Unsupervised Machine Learning (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes (MeSH)</term><term>Apprentissage machine non supervisé (MeSH)</term><term>Carcinome canalaire du sein (classification)</term><term>Carcinome canalaire du sein (imagerie diagnostique)</term><term>Carcinome canalaire du sein (métabolisme)</term><term>Coloration et marquage (MeSH)</term><term>Femelle (MeSH)</term><term>Humains (MeSH)</term><term>Immunohistochimie (méthodes)</term><term>Immunohistochimie (statistiques et données numériques)</term><term>Interprétation d'images assistée par ordinateur (méthodes)</term><term>Interprétation d'images assistée par ordinateur (statistiques et données numériques)</term><term>Noyau de la cellule (anatomopathologie)</term><term>Noyau de la cellule (classification)</term><term>Noyau de la cellule (métabolisme)</term><term>Récepteurs des oestrogènes (métabolisme)</term><term>Récepteurs à la progestérone (métabolisme)</term><term>Tumeurs du sein (classification)</term><term>Tumeurs du sein (imagerie diagnostique)</term><term>Tumeurs du sein (métabolisme)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Receptors, Estrogen</term><term>Receptors, Progesterone</term></keywords><keywords scheme="MESH" qualifier="anatomopathologie" xml:lang="fr"><term>Noyau de la cellule</term></keywords><keywords scheme="MESH" qualifier="classification" xml:lang="en"><term>Breast Neoplasms</term><term>Carcinoma, Ductal, Breast</term><term>Cell Nucleus</term></keywords><keywords scheme="MESH" qualifier="diagnostic imaging" xml:lang="en"><term>Breast Neoplasms</term><term>Carcinoma, Ductal, Breast</term></keywords><keywords scheme="MESH" qualifier="imagerie diagnostique" xml:lang="fr"><term>Carcinome canalaire du sein</term><term>Tumeurs du sein</term></keywords><keywords scheme="MESH" qualifier="metabolism" xml:lang="en"><term>Breast Neoplasms</term><term>Carcinoma, Ductal, Breast</term><term>Cell Nucleus</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Image Interpretation, Computer-Assisted</term><term>Immunohistochemistry</term></keywords><keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Carcinome canalaire du sein</term><term>Noyau de la cellule</term><term>Récepteurs des oestrogènes</term><term>Récepteurs à la progestérone</term><term>Tumeurs du sein</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Immunohistochimie</term><term>Interprétation d'images assistée par ordinateur</term></keywords><keywords scheme="MESH" qualifier="pathology" xml:lang="en"><term>Cell Nucleus</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Image Interpretation, Computer-Assisted</term><term>Immunohistochemistry</term></keywords><keywords scheme="MESH" qualifier="statistiques et données numériques" xml:lang="fr"><term>Immunohistochimie</term><term>Interprétation d'images assistée par ordinateur</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Female</term><term>Humans</term><term>Staining and Labeling</term><term>Unsupervised Machine Learning</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Apprentissage machine non supervisé</term><term>Coloration et marquage</term><term>Femelle</term><term>Humains</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND AND OBJECTIVE</b></p><p>This paper presents an improved scheme able to perform accurate segmentation and classification of cancer nuclei in immunohistochemical (IHC) breast tissue images in order to provide quantitative evaluation of estrogen or progesterone (ER/PR) receptor status that will assist pathologists in cancer diagnostic process.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>The proposed segmentation method is based on adaptive local thresholding and an enhanced morphological procedure, which are applied to extract all stained nuclei regions and to split overlapping nuclei. In fact, a new segmentation approach is presented here for cell nuclei detection from the IHC image using a modified Laplacian filter and an improved watershed algorithm. Stromal cells are then removed from the segmented image using an adaptive criterion in order to get fast tumor nuclei recognition. Finally, unsupervised classification of cancer nuclei is obtained by the combination of four common color separation techniques for a subsequent Allred cancer scoring.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>Experimental results on various IHC tissue images of different cancer affected patients, demonstrate the effectiveness of the proposed scheme when compared to the manual scoring of pathological experts. A statistical analysis is performed on the whole image database between immuno-score of manual and automatic method, and compared with the scores that have reached using other state-of-art segmentation and classification strategies. According to the performance evaluation, we recorded more than 98% for both accuracy of detected nuclei and image cancer scoring over the truths provided by experienced pathologists which shows the best correlation with the expert's score (Pearson's correlation coefficient = 0.993, p-value < 0.005) and the lowest computational total time of 72.3 s/image (±1.9) compared to recent studied methods.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>The proposed scheme can be easily applied for any histopathological diagnostic process that needs stained nuclear quantification and cancer grading. Moreover, the reduced processing time and manual interactions of our procedure can facilitate its implementation in a real-time device to construct a fully online evaluation system of IHC tissue images.</p></div></front></TEI><affiliations><list><country><li>France</li><li>Tunisie</li></country><region><li>Bourgogne-Franche-Comté</li><li>Franche-Comté</li><li>Gouvernorat de Tunis</li></region><settlement><li>Tunis</li></settlement></list><tree><country name="Tunisie"><noRegion><name sortKey="Mouelhi, Aymen" sort="Mouelhi, Aymen" uniqKey="Mouelhi A" first="Aymen" last="Mouelhi">Aymen Mouelhi</name></noRegion><name sortKey="Doghri, Raoudha" sort="Doghri, Raoudha" uniqKey="Doghri R" first="Raoudha" last="Doghri">Raoudha Doghri</name><name sortKey="Mrad, Karima" sort="Mrad, Karima" uniqKey="Mrad K" first="Karima" last="Mrad">Karima Mrad</name><name sortKey="Rmili, Hana" sort="Rmili, Hana" uniqKey="Rmili H" first="Hana" last="Rmili">Hana Rmili</name><name sortKey="Sayadi, Mounir" sort="Sayadi, Mounir" uniqKey="Sayadi M" first="Mounir" last="Sayadi">Mounir Sayadi</name></country><country name="France"><region name="Bourgogne-Franche-Comté"><name sortKey="Ali, Jaouher Ben" sort="Ali, Jaouher Ben" uniqKey="Ali J" first="Jaouher Ben" last="Ali">Jaouher Ben Ali</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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