{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise 6.2: Writing your own segmentation function\n", "\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you probably noticed during the previous two lessons, there are often a lot of small operations that are sometimes necessary before you can even extract the useful data from the image! Rather than writing out each step whenever you want to process an image, you should write a boilerplate function that can be used to segment *any* phase contrast image of bacteria. Your function should execute the following steps.\n", "\n", ">1. Correct for \"hot\" or \"bad\" pixels in an image. \n", "2. Correct for uneven illumination. \n", "3. Perform a thresholding operation.\n", "4. Remove bacteria or objects near/touching the image border. \n", "5. Remove objects that are too large (or too small) to be bacteria. Think carefully! For a multipurpose function, would you *always* want the same area cutoff?\n", "6. Remove improperly segmented cells.\n", "7. Return a labeled segmentation mask. \n", "\n", "A set of discrete operations like this is called a **pipeline**. It is usefull to think of image processing, or data processing for that matter, as a pipeline consisting of successive steps. This helps codify how you are extracting conclusions from your data and improves reproducibility.\n", "\n", "After putting your pipeline into a function, run the pipeline on both the *Bacillus subtilis* ([Lesson 44](l44_intro_to_image_processing.ipynb)) and the *E. coli* [Lesson 45](l45_segmentation.ipynb) image sets. Does your function work well in both cases? Note that in both of these image sets, the interpixel distance is 0.0626 µm per pixel. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solution \n", "\n", "
" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", " var py_version = '3.1.1'.replace('rc', '-rc.');\n", " var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n", " var reloading = false;\n", " var Bokeh = root.Bokeh;\n", " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", "\n", " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) {\n", " if (callback != null)\n", " callback();\n", " });\n", " } finally {\n", " delete root._bokeh_onload_callbacks;\n", " }\n", " console.debug(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", " if (css_urls == null) css_urls = [];\n", " if (js_urls == null) js_urls = [];\n", " if (js_modules == null) js_modules = [];\n", " if (js_exports == null) js_exports = {};\n", "\n", " root._bokeh_onload_callbacks.push(callback);\n", "\n", " if (root._bokeh_is_loading > 0) {\n", " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " if (!reloading) {\n", " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " }\n", "\n", " function on_load() {\n", " root._bokeh_is_loading--;\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", " run_callbacks()\n", " }\n", " }\n", " window._bokeh_on_load = on_load\n", "\n", " function on_error() {\n", " console.error(\"failed to load \" + url);\n", " }\n", "\n", " var skip = [];\n", " if (window.requirejs) {\n", " window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n", " require([\"jspanel\"], function(jsPanel) {\n", "\twindow.jsPanel = jsPanel\n", "\ton_load()\n", " })\n", " require([\"jspanel-modal\"], function() {\n", "\ton_load()\n", " })\n", " require([\"jspanel-tooltip\"], function() {\n", "\ton_load()\n", " })\n", " require([\"jspanel-hint\"], function() {\n", "\ton_load()\n", " })\n", " require([\"jspanel-layout\"], function() {\n", "\ton_load()\n", " })\n", " require([\"jspanel-contextmenu\"], function() {\n", "\ton_load()\n", " })\n", " require([\"jspanel-dock\"], function() {\n", "\ton_load()\n", " })\n", " require([\"gridstack\"], function(GridStack) {\n", "\twindow.GridStack = GridStack\n", "\ton_load()\n", " })\n", " require([\"notyf\"], function() {\n", "\ton_load()\n", " })\n", " root._bokeh_is_loading = css_urls.length + 9;\n", " } else {\n", " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", " }\n", "\n", " var existing_stylesheets = []\n", " var links = document.getElementsByTagName('link')\n", " for (var i = 0; i < links.length; i++) {\n", " var link = links[i]\n", " if (link.href != null) {\n", "\texisting_stylesheets.push(link.href)\n", " }\n", " }\n", " for (var i = 0; i < css_urls.length; i++) {\n", " var url = css_urls[i];\n", " if (existing_stylesheets.indexOf(url) !== -1) {\n", "\ton_load()\n", "\tcontinue;\n", " }\n", " const element = document.createElement(\"link\");\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.rel = \"stylesheet\";\n", " element.type = \"text/css\";\n", " element.href = url;\n", " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", " document.body.appendChild(element);\n", " } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n", " var urls = ['https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n", " for (var i = 0; i < urls.length; i++) {\n", " skip.push(urls[i])\n", " }\n", " } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n", " var urls = ['https://cdn.holoviz.org/panel/1.1.0/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n", " for (var i = 0; i < urls.length; i++) {\n", " skip.push(urls[i])\n", " }\n", " } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n", " var urls = ['https://cdn.holoviz.org/panel/1.1.0/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n", " for (var i = 0; i < urls.length; i++) {\n", " skip.push(urls[i])\n", " }\n", " } var existing_scripts = []\n", " var scripts = document.getElementsByTagName('script')\n", " for (var i = 0; i < scripts.length; i++) {\n", " var script = scripts[i]\n", " if (script.src != null) {\n", "\texisting_scripts.push(script.src)\n", " }\n", " }\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", "\tif (!window.requirejs) {\n", "\t on_load();\n", "\t}\n", "\tcontinue;\n", " }\n", " var element = document.createElement('script');\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.src = url;\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.head.appendChild(element);\n", " }\n", " for (var i = 0; i < js_modules.length; i++) {\n", " var url = js_modules[i];\n", " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", "\tif (!window.requirejs) {\n", "\t on_load();\n", "\t}\n", "\tcontinue;\n", " }\n", " var element = document.createElement('script');\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.src = url;\n", " element.type = \"module\";\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.head.appendChild(element);\n", " }\n", " for (const name in js_exports) {\n", " var url = js_exports[name];\n", " if (skip.indexOf(url) >= 0 || root[name] != null) {\n", "\tif (!window.requirejs) {\n", "\t on_load();\n", "\t}\n", "\tcontinue;\n", " }\n", " var element = document.createElement('script');\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.type = \"module\";\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " element.textContent = `\n", " import ${name} from \"${url}\"\n", " window.${name} = ${name}\n", " window._bokeh_on_load()\n", " `\n", " document.head.appendChild(element);\n", " }\n", " if (!js_urls.length && !js_modules.length) {\n", " on_load()\n", " }\n", " };\n", "\n", " function inject_raw_css(css) {\n", " const element = document.createElement(\"style\");\n", " element.appendChild(document.createTextNode(css));\n", " document.body.appendChild(element);\n", " }\n", "\n", " var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.holoviz.org/panel/1.1.0/dist/panel.min.js\"];\n", " var js_modules = [];\n", " var js_exports = {};\n", " var css_urls = [];\n", " var inline_js = [ function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", "function(Bokeh) {} // ensure no trailing comma for IE\n", " ];\n", "\n", " function run_inline_js() {\n", " if ((root.Bokeh !== undefined) || (force === true)) {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }\n", " // Cache old bokeh versions\n", " if (Bokeh != undefined && !reloading) {\n", "\tvar NewBokeh = root.Bokeh;\n", "\tif (Bokeh.versions === undefined) {\n", "\t Bokeh.versions = new Map();\n", "\t}\n", "\tif (NewBokeh.version !== Bokeh.version) {\n", "\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", "\t}\n", "\troot.Bokeh = Bokeh;\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " }\n", " root._bokeh_is_initializing = false\n", " }\n", "\n", " function load_or_wait() {\n", " // Implement a backoff loop that tries to ensure we do not load multiple\n", " // versions of Bokeh and its dependencies at the same time.\n", " // In recent versions we use the root._bokeh_is_initializing flag\n", " // to determine whether there is an ongoing attempt to initialize\n", " // bokeh, however for backward compatibility we also try to ensure\n", " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", " // before older versions are fully initialized.\n", " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", " root._bokeh_is_initializing = false;\n", " root._bokeh_onload_callbacks = undefined;\n", " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", " load_or_wait();\n", " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", " setTimeout(load_or_wait, 100);\n", " } else {\n", " Bokeh = root.Bokeh;\n", " bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", " root._bokeh_is_initializing = true\n", " root._bokeh_onload_callbacks = []\n", " if (!reloading && (!bokeh_loaded || is_dev)) {\n", "\troot.Bokeh = undefined;\n", " }\n", " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", "\trun_inline_js();\n", " });\n", " }\n", " }\n", " // Give older versions of the autoload script a head-start to ensure\n", " // they initialize before we start loading newer version.\n", " setTimeout(load_or_wait, 100)\n", "}(window));" ], "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.1.1'.replace('rc', '-rc.');\n var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n var reloading = false;\n var Bokeh = root.Bokeh;\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n })\n require([\"jspanel-modal\"], function() {\n\ton_load()\n })\n require([\"jspanel-tooltip\"], function() {\n\ton_load()\n })\n require([\"jspanel-hint\"], function() {\n\ton_load()\n })\n require([\"jspanel-layout\"], function() {\n\ton_load()\n })\n require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n })\n require([\"jspanel-dock\"], function() {\n\ton_load()\n })\n require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n })\n require([\"notyf\"], function() {\n\ton_load()\n })\n root._bokeh_is_loading = css_urls.length + 9;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.1.0/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.1.0/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } var existing_scripts = []\n var scripts = document.getElementsByTagName('script')\n for (var i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n\texisting_scripts.push(script.src)\n }\n }\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n var url = js_exports[name];\n if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n 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\n", "\n", "\n", "\n", " \n", " \n", "\n", "\n", "\n", "\n", "
\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "\n", "import skimage.io\n", "import skimage.segmentation\n", "import skimage.morphology\n", "\n", "import holoviews as hv\n", "hv.extension('bokeh')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Many of our image processing functions will come from `scikit-image`. The function below calls on functions from these packages directly. This is a wonderful example of the power of modular programming—each operation performs a single task!" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def cell_segmenter(\n", " im,\n", " thresh=\"otsu\",\n", " radius=20.0,\n", " image_mode=\"phase\",\n", " area_bounds=(0, 1e7),\n", " ecc_bounds=(0, 1),\n", "):\n", " \"\"\"\n", " This function segments a given image via thresholding and returns\n", " a labeled segmentation mask.\n", " \n", " Parameters\n", " ----------\n", " im : 2d-array\n", " Image to be segmented. This may be of either float or integer\n", " data type.\n", " thresh : int, float, or 'otsu'\n", " Value used during thresholding operation. This can either be a value \n", " (`int` or `float`) or 'otsu'. If 'otsu', the threshold value will be \n", " determined automatically using Otsu's thresholding method.\n", " radius : float\n", " Radius for gaussian blur for background subtraction. Default value\n", " is 20.\n", " image_mode : 'phase' or 'fluorescence'\n", " Mode of microscopy used to capture the image. If 'phase', objects with \n", " intensity values *lower* than the provided threshold will be selected. \n", " If `fluorescence`, values *greater* than the provided threshold will be \n", " selected. Default value is 'phase'.\n", " area_bounds : tuple of ints.\n", " Range of areas of acceptable objects. This should be provided in units \n", " of square pixels.\n", " ecc_bounds : tuple of floats\n", " Range of eccentricity values of acceptable objects. These values should\n", " range between 0.0 and 1.0.\n", " \n", " Returns\n", " -------\n", " im_labeled : 2d-array, int\n", " Labeled segmentation mask.\n", " \"\"\"\n", " # Apply a median filter to remove hot pixels.\n", " med_selem = skimage.morphology.square(3)\n", " im_filt = skimage.filters.median(im, footprint=med_selem)\n", "\n", " # Perform gaussian subtraction\n", " im_sub = bg_subtract(im_filt, radius)\n", "\n", " # Determine the thresholding method.\n", " if thresh == \"otsu\":\n", " thresh = skimage.filters.threshold_otsu(im_sub)\n", "\n", " # Determine the image mode and apply threshold.\n", " if image_mode == \"phase\":\n", " im_thresh = im_sub < thresh\n", " elif image_mode == \"fluorescence\":\n", " im_thresh = im_sub > thresh\n", " else:\n", " raise ValueError(\n", " \"image mode not recognized. Must be 'phase'\" + \" or 'fluorescence'\"\n", " )\n", "\n", " # Label the objects.\n", " im_label = skimage.measure.label(im_thresh)\n", "\n", " # Apply the area and eccentricity bounds.\n", " im_filt = area_ecc_filter(im_label, area_bounds, ecc_bounds)\n", "\n", " # Remove objects touching the border.\n", " im_border = skimage.segmentation.clear_border(im_filt, buffer_size=5)\n", "\n", " # Relabel the image.\n", " im_border = im_border > 0\n", "\n", " return skimage.measure.label(im_border)\n", "\n", "\n", "def bg_subtract(im, radius):\n", " \"\"\"\n", " Subtracts a gaussian blurred image from itself smoothing uneven \n", " illumination.\n", " \n", " Parameters\n", " ----------\n", " im : 2d-array\n", " Image to be subtracted\n", " radius : int or float\n", " Radius of gaussian blur\n", " \n", " Returns\n", " -------\n", " im_sub : 2d-array, float\n", " Background subtracted image.\n", " \"\"\"\n", " # Apply the gaussian filter.\n", " im_filt = skimage.filters.gaussian(im, radius)\n", "\n", " # Ensure the original image is a float\n", " if np.max(im) > 1.0:\n", " im = skimage.img_as_float(im)\n", "\n", " return im - im_filt\n", "\n", "\n", "def area_ecc_filter(im, area_bounds, ecc_bounds):\n", " \"\"\"\n", " Filters objects in an image based on their areas.\n", " \n", " Parameters\n", " ----------\n", " im : 2d-array, int\n", " Labeled segmentation mask to be filtered. \n", " area_bounds : tuple of ints\n", " Range of areas in which acceptable objects exist. This should be \n", " provided in units of square pixels.\n", " ecc_bounds : tuple of floats \n", " Range of eccentricities in which acceptable objects exist. This should be \n", " provided on the range of 0 to 1.0.\n", " \n", " Returns\n", " -------\n", " im_relab : 2d-array, int\n", " The relabeled, filtered image.\n", " \"\"\"\n", "\n", " # Extract the region props of the objects.\n", " props = skimage.measure.regionprops(im)\n", "\n", " # Extract the areas and labels.\n", " areas = np.array([prop.area for prop in props])\n", " eccs = np.array([prop.eccentricity for prop in props])\n", " labels = np.array([prop.label for prop in props])\n", "\n", " # Make an empty image to add the approved cells.\n", " im_approved = np.zeros_like(im)\n", "\n", " # Threshold the objects based on area and eccentricity\n", " for i, _ in enumerate(areas):\n", " if (\n", " areas[i] > area_bounds[0]\n", " and areas[i] < area_bounds[1]\n", " and eccs[i] > ecc_bounds[0]\n", " and eccs[i] < ecc_bounds[1]\n", " ):\n", " im_approved += im == labels[i]\n", "\n", " # Relabel the image.\n", " return skimage.measure.label(im_approved > 0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the previous code block, we defined a few functions. I could have thrown all components into a single function, but keeping modularity is important. The filtering functions will make application of area and eccentricity bounds easier especially if I want to use them again in the future. The important function here is `cell_segmenter()` which actually calls the two filtering functions we wrote above! Let's try them out on some test images. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Load a B. subtilis and E. coli test image.\n", "ecoli = skimage.io.imread(\"data/HG105_images/noLac_phase_0004.tif\")\n", "bsub_phase = skimage.io.imread(\"data/bsub_100x_phase.tif\")\n", "bsub_fluo = skimage.io.imread(\"data/bsub_100x_cfp.tif\")\n", "\n", "# Using my knowledge of biology, we can draw some bounds.\n", "ip_dist = 0.0626 # in units of µm per pixel.\n", "area_bounds = (1 / ip_dist ** 2, 10.0 / ip_dist ** 2)\n", "ecc_bounds = (0.8, 1.0) # they are certainly not spheres.\n", "\n", "# Pass all images through our function.\n", "ecoli_seg = cell_segmenter(\n", " ecoli, area_bounds=area_bounds, ecc_bounds=ecc_bounds\n", ")\n", "bsub_phase_seg = cell_segmenter(\n", " bsub_phase,\n", " image_mode=\"phase\",\n", " area_bounds=area_bounds,\n", " ecc_bounds=ecc_bounds,\n", ")\n", "bsub_fluo_seg = cell_segmenter(\n", " bsub_fluo,\n", " image_mode=\"fluorescence\",\n", " area_bounds=area_bounds,\n", " ecc_bounds=ecc_bounds,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can make merged images to see how well we segmented all of the objects. Since this would be hard to see on the *Bacillus* fluorescence image directly, we'll take a look at the fluorescence segmentation on the corresponding phase image." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.holoviews_exec.v0+json": "", "text/html": [ "
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\n", "" ], "text/plain": [ ":Layout\n", " .RGB.I :RGB [x,y] (R,G,B)\n", " .RGB.II :RGB [x,y] (R,G,B)\n", " .RGB.III :RGB [x,y] (R,G,B)" ] }, "execution_count": 4, "metadata": { "application/vnd.holoviews_exec.v0+json": { "id": "p1002" } }, "output_type": "execute_result" } ], "source": [ "# Make the two phase images as floats and copy them.\n", "ecoli_float = ecoli / ecoli.max()\n", "bsub_float = bsub_phase / bsub_phase.max()\n", "ecoli_copy = np.copy(ecoli_float)\n", "bsub_copy = np.copy(bsub_float)\n", "bsub_copy2 = np.copy(bsub_float)\n", "\n", "# Mark the segmented bacteria on the copied images.\n", "ecoli_copy[ecoli_seg > 0] = 0.8\n", "bsub_copy[bsub_phase_seg > 0] = 0.8\n", "bsub_copy2[bsub_fluo_seg > 0] = 0.8\n", "\n", "# Merge them into RGB images.\n", "ecoli_merge = np.dstack((ecoli_copy, ecoli_float, ecoli_float))\n", "bsub_phase_merge = np.dstack((bsub_copy, bsub_float, bsub_float))\n", "bsub_fluo_merge = np.dstack((bsub_copy2, bsub_float, bsub_float))\n", "\n", "# Let's plot 'em!\n", "frame_width = 200\n", "frame_height = int(frame_width * ecoli.shape[0] / ecoli.shape[1])\n", "opts = dict(\n", " frame_height=frame_height, frame_width=frame_width, xlabel=\"\", ylabel=\"\"\n", ")\n", "plots = [\n", " hv.RGB(im).opts(**opts)\n", " for im in [ecoli_merge, bsub_phase_merge, bsub_fluo_merge]\n", "]\n", "\n", "hv.Layout(plots).cols(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's actually pretty good! It's obvious that segmenting in fluorescence yields a better mask than in phase. Keep that in mind for your own experiments!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Computing environment" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python implementation: CPython\n", "Python version : 3.11.3\n", "IPython version : 8.12.0\n", "\n", "numpy : 1.24.3\n", "skimage : 0.20.0\n", "bokeh : 3.1.1\n", "holoviews : 1.16.2\n", "jupyterlab: 3.6.3\n", "\n" ] } ], "source": [ "%load_ext watermark\n", "%watermark -v -p numpy,skimage,bokeh,holoviews,jupyterlab" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.3" } }, "nbformat": 4, "nbformat_minor": 4 }