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Code de segmentation des noyaux utilis\u00e9s dans la recherche sur l\u2019analyse d\u2019images de pathologie num\u00e9rique, 2024 [MIT License].<\/p>\n
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# nuclei segmentation in histopathology slide<\/p>\n
thresholded = image > otsu(image)<\/p>\n
labels = measure.label(thresholded)<\/p>\n
nuclei = morphology.remove_small_objects (labels, min_size=100)<\/p>\n
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En pathologie, les sp\u00e9cialistes examinent traditionnellement des \u00e9chantillons de tissus au microscope pour identifier les maladies. Aujourd\u2019hui, des programmes informatiques aident cette analyse, effectuant des t\u00e2ches qui reposaient autrefois uniquement sur l\u2019expertise humaine. Cet extrait de code illustre cette transformation ; quatre lignes qui segmentent les noyaux cellulaires, \u00e9tape fondamentale du diagnostic. <\/p>\n
Ces lignes fonctionnent comme un assistant num\u00e9rique au microscope. Le code identifie d\u2019abord les cellules et l\u2019espace vide, comme distinguer des taches d\u2019encre sur du papier blanc. Ensuite, il regroupe les pixels connect\u00e9s pour former des noyaux complets, de la m\u00eame fa\u00e7on que nous identifions des types de nuage. Enfin, il \u00e9limine le bruit, ne conservant que les structures significatives. Le r\u00e9sultat est une carte que les ordinateurs peuvent analyser, transformant l\u2019image complexe vue par le pathologiste en donn\u00e9es organis\u00e9es permettant aux syst\u00e8mes d\u2019intelligence artificielle de d\u00e9tecter des motifs canc\u00e9reux, classifier les maladies ou pr\u00e9dire les r\u00e9ponses aux traitements. <\/p>\n
Cet extrait marque un tournant m\u00e9dical, un t\u00e9moignage de soins de sant\u00e9 devenus computationnels, avec du code source int\u00e9gr\u00e9 dans des processus guidant des d\u00e9cisions vitales. Il refl\u00e8te notre d\u00e9pendance croissante aux machines, par n\u00e9cessit\u00e9 : des millions de patients, trop peu de pathologistes form\u00e9s. \u00c9crit pour des prototypes de recherche, ce code accompagne d\u00e9sormais le diagnostic du cancer, la d\u00e9couverte de m\u00e9dicaments et le d\u00e9pistage par intelligence artificielle. Cette transformation de la biologie vers les donn\u00e9es, de l\u2019observation humaine vers l\u2019analyse computationnelle, impacte notre compr\u00e9hension de la pratique m\u00e9dicale \u2013 o\u00f9 des fragments de code, une fois mis \u00e0 l\u2019\u00e9chelle, portent \u00e0 la fois un pouvoir et une responsabilit\u00e9 \u00e9thique. <\/p>\n
Cette transformation de la biologie vers les donn\u00e9es, de l\u2019observation humaine vers l\u2019analyse computationnelle, impacte notre compr\u00e9hension de la pratique m\u00e9dicale \u2013 o\u00f9 des fragments de code, une fois mis \u00e0 l\u2019\u00e9chelle, portent \u00e0 la fois un pouvoir et une responsabilit\u00e9 \u00e9thique.<\/p>\n
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Esha Sadia Nasir, doctorante \u00e0 l\u2019Universit\u00e9 de Warwick, est sp\u00e9cialiste en pathologie computationnelle.
Ses recherches portent sur l\u2019analyse d\u2019images d\u2019immunofluorescence multiplex\u00e9e pour l\u2019identification de biomarqueurs et l\u2019\u00e9tude de la progression des maladies. Elle d\u00e9veloppe des algorithmes pour analyser des lames d\u2019histopathologie, contribuant ainsi aux avanc\u00e9es dela recherche sur le diagnostic et le traitement du cancer. Her research focuses on multiplex immunofluorescence image analysis for biomarker discovery and disease progression. She develops algorithms to analyze histopathology slides, contributing to advances in cancer diagnosis and treatment research.<\/p>\n
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#Pathologie computationnelle<\/strong><\/p>\n [\/et_pb_text][et_pb_text _builder_version=\u00a0\u00bb4.27.5″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb text_font=\u00a0\u00bb||||||||\u00a0\u00bb text_font_size=\u00a0\u00bb26px\u00a0\u00bb header_font=\u00a0\u00bbEscuela-regular||||||||\u00a0\u00bb header_font_size=\u00a0\u00bb27px\u00a0\u00bb background_color=\u00a0\u00bb#FCEC45″ width=\u00a0\u00bb79%\u00a0\u00bb custom_padding=\u00a0\u00bb20px||20px|20px|true|false\u00a0\u00bb border_radii=\u00a0\u00bbon|100px|100px|100px|100px\u00a0\u00bb border_width_all=\u00a0\u00bb2px\u00a0\u00bb border_color_all=\u00a0\u00bb#000000″ global_colors_info=\u00a0\u00bb{}\u00a0\u00bb]<\/p>\n #Healthcare<\/strong><\/p>\n [\/et_pb_text][et_pb_text _builder_version=\u00a0\u00bb4.27.5″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb text_font=\u00a0\u00bb||||||||\u00a0\u00bb text_font_size=\u00a0\u00bb26px\u00a0\u00bb header_font=\u00a0\u00bbEscuela-regular||||||||\u00a0\u00bb header_font_size=\u00a0\u00bb27px\u00a0\u00bb background_color=\u00a0\u00bb#FCEC45″ width=\u00a0\u00bb79%\u00a0\u00bb custom_padding=\u00a0\u00bb20px||20px|20px|true|false\u00a0\u00bb border_radii=\u00a0\u00bbon|100px|100px|100px|100px\u00a0\u00bb border_width_all=\u00a0\u00bb2px\u00a0\u00bb border_color_all=\u00a0\u00bb#000000″ global_colors_info=\u00a0\u00bb{}\u00a0\u00bb]<\/p>\n #Imagerie m\u00e9dicale<\/strong><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" Code de segmentation des noyaux utilis\u00e9s dans la recherche sur l\u2019analyse d\u2019images de pathologie num\u00e9rique, 2024 [MIT License].# nuclei segmentation in histopathology slide thresholded = image > otsu(image) labels = measure.label(thresholded) nuclei = morphology.remove_small_objects (labels, min_size=100)\u00c0 propos du codeEn pathologie, les sp\u00e9cialistes examinent traditionnellement des \u00e9chantillons de tissus au microscope pour identifier les maladies. Aujourd\u2019hui, […]<\/p>\n","protected":false},"author":4,"featured_media":1058,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[108],"tags":[116,115,117],"class_list":["post-877","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-miroir-de-la-societe","tag-imagerie-medicale","tag-pathologie-computationnelle","tag-sante"],"_links":{"self":[{"href":"https:\/\/www.sourcecode-exhibition.softwareheritage.org\/fr\/wp-json\/wp\/v2\/posts\/877","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.sourcecode-exhibition.softwareheritage.org\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.sourcecode-exhibition.softwareheritage.org\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.sourcecode-exhibition.softwareheritage.org\/fr\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/www.sourcecode-exhibition.softwareheritage.org\/fr\/wp-json\/wp\/v2\/comments?post=877"}],"version-history":[{"count":4,"href":"https:\/\/www.sourcecode-exhibition.softwareheritage.org\/fr\/wp-json\/wp\/v2\/posts\/877\/revisions"}],"predecessor-version":[{"id":906,"href":"https:\/\/www.sourcecode-exhibition.softwareheritage.org\/fr\/wp-json\/wp\/v2\/posts\/877\/revisions\/906"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.sourcecode-exhibition.softwareheritage.org\/fr\/wp-json\/wp\/v2\/media\/1058"}],"wp:attachment":[{"href":"https:\/\/www.sourcecode-exhibition.softwareheritage.org\/fr\/wp-json\/wp\/v2\/media?parent=877"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.sourcecode-exhibition.softwareheritage.org\/fr\/wp-json\/wp\/v2\/categories?post=877"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.sourcecode-exhibition.softwareheritage.org\/fr\/wp-json\/wp\/v2\/tags?post=877"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}