{"cells":[{"metadata":{},"cell_type":"markdown","source":"# In this kernel, I'm showing how to approximate lung volume with trapezoidal rule and threshold-based per-slice lung area detection (inspired from https://www.kaggle.com/miklgr500/lung-volume-feature)"},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import os\n\nimport numpy as np\nimport pandas as pd\nimport pydicom\n\nfrom skimage.measure import label,regionprops\nfrom skimage.segmentation import clear_border\nfrom tqdm.notebook import tqdm \nfrom multiprocessing import Pool\n\nimport matplotlib.pyplot as plt","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## What is Trapezoidal Rule ?\n\n### Trapezoidal rule is a technique for approximating the definite integral."},{"metadata":{},"cell_type":"markdown","source":"![ff1e6d438c8e663ed8ab18d19b011371c24b3ac7.svg](attachment:ff1e6d438c8e663ed8ab18d19b011371c24b3ac7.svg)","attachments":{"ff1e6d438c8e663ed8ab18d19b011371c24b3ac7.svg":{"image/svg+xml":"<svg xmlns:xlink="http://www.w3.org/1999/xlink" width="30.85ex" height="6.343ex" style="vertical-align: -2.338ex;" viewBox="0 -1724.2 13282.7 2730.8" role="img" focusable="false" xmlns="http://www.w3.org/2000/svg" aria-labelledby="MathJax-SVG-1-Title">
<title id="MathJax-SVG-1-Title">{\displaystyle \int _{a}^{b}f(x)\,dx\approx (b-a)\cdot {\tfrac {f(a)+f(b)}{2}}}</title>
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aU1GftmhFjOX5nM1WjCXVhVnP5Dgu6QSKAPe06fT58+XONse1YMTiVPVcKBxJ0eOWNIGuCgd/h4P25uVv2CMxT5D89OnIYPNrdT0W0lPA71FpEVtKWkTN+FWUP5zi33ZJ9zqjTE/TbJwM/qUKuBEpI0+hUN2gbeB24Be/qswLgPOGwRr42GbhxWUXW0tjwJpnhjYjDFnJFlVgBmAgztbjHjoGpwkj0mzg+m77jokGGrYzwaC+xrr7Gcwe4DZ0WJ3ANPPrcSCYyFkYAOw2WC/cixfBzCrQsbWNNalfLLiz6PrenJTfgF96uE3DkwHgn69NgMP1cJVI2BTXGx0YWFg2OrjZhjLZT6dnDVYTqkqzv6XvigKOJG4lz6tIiMlFDrRMWakwR5q4GDrhPpiAhhrARMedLVt6JVt8sd8kyM1BhMeoBXAkNY7FMrML6828B/H2H8SCr62917m3VcLRzf2hHaN15I2LRkxraJf49ehJyyc6vP08qANTfikZNwyfWsUcCJxKeOSuTuZ+l4nG2PHA2NwQoOsaR5jnSAIZFnIMtacScBh5Wo2ZRaUv24tL9vG4N+rZ43+MtnadyX0rYUbgavxv3zRCNztwG/SYWu8bPfQ3LJ9G5zQC8DBPk+/29DIyVWzxn2tb5ACTiTuQi3QmPZjA2faBsZhbK8YbUo/LBMMTDApoXszC8r+ZeFp25DyZDKE3acwvtYdsxZ3JW1a/HwUlB0dsOZZYHfvcxbmpQWCZ1XNGr1J3yQFnEjcyMwtO46AmWoamGShbzt7Zw0GVln4DthsrN1kMQ2Y/90ssbOF/gHY20LfNryuwZqjDBxlUkK/z8wvfxFjSqp2XzSPwkInwYKtv3ULjV6I/914DcCMOrhxBNTH07YPKyj7mbHmYaC3zy56aCdn54veLT68Qd8mBZxIzA0+v6J3Wp/QeVhzBdjh4b/Co7EB+ABj/m3h/YANVYUaUz+rXr9mFXMmRzVj/YhpFf22Grtf0LEHWOyBxjLCurfED2rlR4PA6Vh7eubq41aQV35/7y2hkg//Mn5zvLd3vJe0iWjS7GDmgAG/w/Irn1AOGWOmVxZl36tvlGigt8TciCmvDKhPC04z1lyMz6kmH7UY/mEsFVhnYeXade9HG2Rt7kkWlA+11pxg3NIqJxN5pvymX6s1GHuvk1L/QPX9EzfEYbDt7sCdBs6N2L5w09AYlrSJJD1/3m5Bgk/iP0XYBmzgp1UlY3rSLfMzgCuAl4GJOpoo4CROZFwyd6dAQ68rwF4B7NzKJ3Uz1rxkcJ7utdl5KSY9pEmzg1kDB55osWcCP6H59E9e31nM3bV9+sz48p5RtXESbq2VtFmEe60t7u44zMgrPyZgmA3s4/P5+DRoQ6d9Ujz+Pz3sa6SAU8BJnAVbL9OYeqmx5ldR9IjetMbM6rOpcXY8nfY7qbAiZeXXoVOAPCzjaGHSBAOfO8b+ellRzpNgYlIuJlFK2kTsSecvuBjs3XgHb4dD2UmtP7P6/olreuDXSQGngJN4kZVXdro15m58Z3b/n1pr7CMY7ls2c+wncf87TZ03hGCwwBrygR0jf9vsP7GBS7tzsPG2kjamlZI2FvIz4It4a9uhuWU7pwRMMXCO7+9n7b072V1+9W5pj72ZRAGngJOYh0DBwiyc0H3WmHEtrPYN2Aed1IYHE/Gv8eEXlQ90HC7F4VIM/SOsFgJz3yZnyw0rS0/d0pXbUwVDg/AQCVDSxrc9py0Y5Tj2cWCwz9ObsEytKsn5Ww//aingFHASKyNzF6duCH53jbHmBvznMgRYb+Bu6nvfW/nH4zYm+u8cHrt3lbFMb2H4QY3B5FYWZy/sgl5bQpW0aca9S/J6DP+H753eZqlj7ZnVJTkf6xumgFPASUxkTis/AodZ+M8wAVCH5Q+pjY13LHl4wtpk+/3Tp80bFHCCvzfwswjfNWuNLantvcOVnXUTSqKVtPEamlu2b0rAPAYcH+GA9XQotf6CeLw7NUbOAsYCHwEPqDkUcNLFTiqsSFm5uvEGMNcTaayl5VkCXFVVlLM82dtjWO7Co0zAeRAY6d8U5j82aM6pfnDMkva+x2JI3QWuBG5qoac8Jw3y42XWf08rmMyChVNw7J0RTu/WWmuuXFaSXaxvmCjgJCYychekBwL2MeBo/4M5y7BctKwkp6ynhf6qr52rrLWF+My8YWCLNeayqqLsWW197U/hEMfttR0WYZUVBnKHupWf4/UzU0rka4UfOJaf6ZSkr4FAf2BzuHcuCjjpCln55f/PujNj+N1J2Ijl7i19+94UL2PCYmH4heWZTpBZRDgFB/bJTU7d1GhuQPkUejtQSCKVtGlq0uxg5sABlxu4OcK1SmvhPptaf031/RO36hvmS9fgFHDSpX+BXzK3l2nodY/BFkT8Cxz7y+rise+rtYDCwkDW18dfba39LZDqs8b7oUDo9JqZ4yPetl8Nx5pWSto4cOEweDVOe22HBQK2GDgiwiqrMeaCqqLsl/WBUcC1lyp6S8d6JPkVgwMNaa9HCDfHGHuXk1p/lMJtu4BzKouyb3eMPQFY4bPGoUEn+M7waQtGeZ9YCX1r4DYDr0UIt0bg9gAcFI/hNmJaxR7D8ssfDgTsOy2E2yOpDY0jFG6iHpzE7q/wgvknBmzgKfxnI/kK6/yiqmTcArVUZIMvr+jfqy40y8KZPk9vtcYULCvK/hO4JW0cKAH2i/BycVnSBsITafduvBzMdUQeDL/CCZi86pnZ8/XJUA9OPTiJmcyC8tyADZT5hZuFeTjBQxRurVtx7+h1lcU5Zxm4BvCW2ullrP3jMec+WbQsEHjIcQ9ifuHWANxeB0fEXbgVFgYy88rPTusdWgLm9xHCzbHwh9RA8CCFm3QmlcuRNh+wslYfd4+1XOrzbAjLzcv2fP2WRKuLFmuVxTl3DCsoW2KseYImU2od/dlbXPzaH/IDjn9zGngrBFOHQXzdYThpdnDYD3Y526w212M4oIU1FzvGXlJdNPZtfQpEAScxk3HJ3F6B1Wl/sTDJ5+nvnIA5R3+Bt9+yorEvuTPm2+d2qV2XMeWtUrKrIt7ZXwvcNCTOStqcVFiRsnJV48/AXI8ls4VVV1nMdcv2WPSo/hgSBZzEOtx2CjSkPYP/WKXKQIjTqoqzq9RSHVNdkvPx2zvucWvfrRtLdqjf7HeHJZ/tMnjN0l2zzpxWNW9RvGx3Zm7FD6xxzl+5OlSAMUNbWLUOwz2pJnjrkpmjN2mPiwJOYmrI1PLdgw3MtT4DiY2187f2STl7xb2j16mlOmYF7OlAkd242rekzea0HXj46FxeOWDirhYzN2vM1XeEUuv/EMtpq4blLTghYMizhM40kWdQAXDA/s00OtdXzhr/qfa2KOAk9n+ZF5QPxdp5FpPh8/Sf99wz5cJXC0c3qqXab1tJm1ALJW3e2fco7j/hctb023Xbon4Wbg40pF2RmV92n0mxpZUPjFvZHdubVVB+sGPNqQb7M7AHtFI8rhHskzjOrVWl45dqb0t30jABiRxueQt/iHFewadytYU7lhVnXxurAp7JIpqSNl/tvMdtU3762BjT8vegEcuLxtgnqe/zcmdWZRgxaXZaw4D+xxsTOM3CafiXr/FqMNY+1hhMubVm5uhq7ekuo2ECCjhpq/AEwfOAnZtlm2V6VUnODLVSh3ptbS5pk5lffg5uL2+fVl6+DlgE5g1r7ZsptvE/n5ROWBVtmG3dZeCBgYAz0mBGhk9LH0zLpx+b+sYY+2djUx5cWjx6hfa0Ak4Bp4CLK1l58w+3BMp8ZnVvMIYLKotyHlMrtd8yGBGAP9KOkjYHnztvh7odAldjzWUtFFX1s95ivgD7DfCNARsOWmPcyXr7A3uHe+vtOS4sMlASSq1/SvNGKuAUcAq4+Oy5TZt/iHECC4ABnk/KZjCTNH1S+3VmSZuhuWU7B4NcYqyZFv7exMIKa+2zNiU4qyOlfqRDxuBOeVYDPKXmUMBJBMNzyw5yAmYhzWcnWe8YO0GDcdtvOfzQur22Ti1p49bec04FewFu4cteXfhrWOAdi3k+YOwLlUU5H2rPigJOAZcAPbey/Y1jXgV28zy10TF2nMKtfbqzpE3WBa/vaFPrJhrseGvMKCCrg5u/3sL7xvCexb6bEgpVRHstT7pNJu70bd8AmtBcASfNem5ujbJX8Z7qMmwmZCdUlY59Xa3UdrEuaZNxydxdgw29DgIny7FkYsyuAWt3tcYMbLKaA6zHmA3AVwa+so5dEcR575OSsdW6Szbu6RpcCzQOrqeHW37FYIfQQm+4GdhirD1lqcKtzVZC31q4Ebga/wnNG4G7A1CY7t7x2DUBe//ENcDC8ENEASc9x4gprwxowHkZ9+65puqtDUyqLBnzqlqpbT6F8bUJWtJGJNmoXE4PNfj8it4NaSnPgR3eLNzgzKqSMXPVSm0Ktv7LoSQhS9qIqAcnSaOwMNBrdegxaznO80yjMYFJVUVjXlQjRa8GTnGg2KcnDMRxSRsRBZwkm2Grj5vhW0Haclll8Zjn1ULRWQ67O+7MIudGWCUuS9qIKOAkOcMtb8F0g72s+TP2pqqSsTPVQlGH2yQLM41PRfOwRcDUdFAJIREFnHS1rPyyyRZ7e/OOG48vK865SS3UuhWwZwhmWjgjwirrLfwqHR7aNh2WiCjgpCt7brkLj7I4j+C5scjCvJ2c/r/UeKeWRVPSBphrIT8DvlCLiSjgpBuMmFaxR4MTegro3XS5gfdSA8Gz3i0+vEGtFFkVDF0OpQayI6zynYVrM6BUrSUSPzRMIMmNzF2c2uCEZtO8xMqKlEDw5CUzR29SK0XstQWqITfojl2LFG5zHMhSuImoByfdbGNg/YPA8Z7FmwKOPW1J8ejVaiF/y2DEcnjYwFERVolY0kZE1IOTLpaZVz4N7IXejonBTllaOvYjtVBzFZBSA9cE4F0ih9ucNBihcBNRD05iYPi0BaMcx97T7AnDbyuLxs5WCzXXVSVtRLrQI8BbgKo8KOB6hvRp8wY5jn0GSNuu64Z9ftnub2g4gEe4pM21Fq4DUn1W6bSSNiKd7IPwQxRwyW9k7uLUjc66v9K8rltlyOE8CgsdtdL3quFYJ4YlbUQ66BjgYOBz3HlQpQldg0syG4PrbgNGeRc7lp8sLx27Xi3kWgl9a+A2A69FCLdG4PYAHKRwkzg2CXce1EvUFOrBJbWMvAWnYu0VnsWONfan1cVjNdFvmEraiKgHJwkkfdq8QQFj/0TzKu23LSsa+5JaSCVtRNSDk4RzUmFFysrVjU8CAz1Pvb2j079QLaSSNiIKOElIq75u/D2YYz2L1waD5pyePg1XtXuzzV2opI2IAk4SS2bewonWOld5FltjAr/85MExn/XkttlW0gaVtBFRwEli2T/3lT1DxnkE73U3yx09uXDpiihK2gC/GQr3G9CwCZEkpZtMEpY1IZPyULPeibH/3NH2/78e2SJgqiE3BEuJHG5zLRyUDn9QuImoBydxKCu/PN9iTvYs/jZknEnvlva8624qaSMiCrgkkJG7IN1i7/AuN9hpNTPH96himxYCNTDVwN1AvwirzXHgomGwRp8eEQWcxKmTCitSVn7d+DjW9POk26M9bRJllbQRkZboGlyCWbm68Qas8R7Qv+xFr8t7ShtEW9KmHg5UuImoBycJYHjBwpGOda7zLHZMgPM+mnn8dz2hDVTSRkQUcElm8PkVvR0behxvORfDvZUzcyqS/fePtqRNPUwfDhv1iZEeYgbwOO7QF1HAJabU3qGbgCzP4o/ra4PXJ/vvXg2jHHiYyCVtahx3mq1X9UmRHubL8EN86BpcAhhesHCkgSs9i+uxgZ+t+PPoumT9vZuUtFlEyyVtDlS4SQ91MnAbcL6aQgGXcE4qrEhxrPOQt7dt4JaqkjFJW8k3XNLmY+CaCJ/Tj4Bj0uHaIVCnT4r0UNnh78hkNYUCLvF6MatC1wGHehZ/mPLt2tuTNNiiLWlzuEraiEhLdA0ujmXmzhuO4deexY3GOlOWzJlcn2y/b2slbSy8bWGKStqIiAIukRUWBlgdfBjo7TnK311ZMi6pei7RlrRJV0kbEVHAJUHvbfWxlwGjPOFWtaVv35uS6fdUSRsRUcD1IBlTK/aB0M2exQ4Bpn55z6jaZPgdV6ikjYgo4HqeQKrzB6xn4mBjHqwqyl6U6L+bBVMD54bgXmCXCKvNtZCfAV/o0yAiCrgkMTy/bLxj7U88i1c1hpyEr/GmkjYi0q2dBTVB/Njnijf7OJiZ3uXGcuny0rEJOxWPhUA15AbhAyKH2xwHshRuIqIeXBLaoXbz9RYzdPtws/MrS8Y+lai/U5QlbS5Oh6f1CRAR9eCS0P5584dZzHTP4q3GMZck4u/TxpI2CjcRUQ8uWYVMoBjotV3vDX639KGchLs9XiVtREQ9OAEgM6/8bGBM02UWlm2tC96ZSL/Hp9C7BgotvBMh3KyB0gY4WOEmIl3NxNn27Ams9CybD4xP1h2wzxVv9ulbu+UTvPMuGjOxqij75UT5PaphlFFJG5Hu1gd3tqMGYJOaY3s6RRljfWs3XwPGM6mwmZ0o4bYS+tbCjcDVEc4INBqY2Rt+vRds0R4X6VS14Yco4OJL+rR5g4xjrraeD2wwyK8SYfvDJW1K8J/1H9ySNlOGuqcsRaTznQecAryHWxdOmtA1uFg2vhOcYaHvdgstt33yYPZncR5sbSlpo3AT6TqHAJOAE9QU6sHFjczcsuOAMz2Lv9hka++K5+0Ol7QpAvbxe14lbUREAdeTTZodJGAewHOTj7FcubL01Li8TqWSNiKigJPWe28/GDAFyw89PZ9Xq0py4nLGEpW0EREFnLRqxLSKfg1OqNCzOGRs4PJ429YVrZe02QDcqJI2IqKAExps6Grc8X7/Y2BWZcmYD+JlG1XSRkQUcNImWRfP38uGuMqzeFNKIFgYR722IcvhIZW0EREFnETNaQz81sAO2/WWjL19yczRq+Og1xaogakhuBs8xVa/N8fCxRnwX+1NEVHACQDDc8sOcuAXnsUr+2xy7on1tqmkjYgkIw307iahgLkTCG7fezM3fPiX8ZtjtU0qaSMi6sFJh2QWlB+PbTZh9IfLvvn20Vhtk0raiIh6cNJxllu8i5yAuZo5k7t9QLRK2oiIenDSSb23BT/CWs88cWZB9czs+d29LdUwylFJG5FkUgusR6Vy1IOLQdfNYG1hs96btf/XnVuxEvrWwG3GnXHEL9waDdzXBw5WuIkklOuB/sBkNYV6cN1qWF75j8Ec6Vn8YnVJzlvdtQ0qaSMi6sFJ5yosDBhjbvR26Rzsjd0UbCppI5L8rgAWA39QU6gH1329t6+PPQeaTaj8dHXx2Pe7+r1V0kakxxgEjESTLyjgus2k2UFjA/8H29XqdoKOvbkr31YlbUREFHBdKmvgLr+wWM/NHPbxpaVjP+qq91RJGxERBVyXGpm7OHWjWXfD9p03QsYEf9cV77dCJW1ERBRw3WFjcF0uliHbLzV/rCwaU9mZ7xNlSZuXLeSppI2IKOCkQwafX9EbG7rWs7g+QODWTu61qaSNiIgCrvuk9Q5dhOfORQPFS4tHr+ikXptK2oiIKOBi0Hsj5C1mWmsxd3TG66ukjYhI22igdydJ7dV4IbCnp8d1f1Vx9lcdeV2VtBERUQ8uZsJ3Tm7XezOwxUmtv6sjrxsuafMw7kBOPysM5A2F+doLIiLqwXW6DcH1P8czHZZjbWn1/RPXtOf1PCVt/MKtaUkbhZuIiHpwXaCwMGBW2+mepQ0hyz3tebloStpYuDAdKtT4IiLqwXWZrNXHngUc4Fn8yPLSsZ+35XWalLR5jVZK2mQo3ETE9SXwvvu3sagH18ks5leeRaFAiDvb8hrL4YRat9eWEWEVlbQRET8zwg9RD65zZeYtnEiza2R29tKHcqKa77EGdl4OJdYtMuoXbippIyKigItF6zm/9nboHKLrvdXAycB/LOQCpnnPkLcdOCQdrh0B9WpsEfFxC7AW+JuaojmdomynYXkLTsDa45ouM4YXq4tarvemkjYi0on64s5Fu6OaQgHXeYy9rtky69ze0o+opI2IiAIurmXklx1qYPz24UZFZcm4N/zWX+GWtHnQwo8jvKRK2oiIKOBiL2DNlc2umgVoVjGgSUmbe4ABEV5OJW1ERBRwsZd18fy9bCOTPYvfrSrKKff02oYsh1IDORFeSiVtREQUcPHDhgIXA2nbLTN2RpNeWzQlbV5Mhbx9YaVaVEREARdze+W+0BdLrmfxV2nffPcUuCVtamCWgaMjvIRK2oiIdBONg2uDfsE+vwQGNl1mjLn/gTmTnW0lbVoIN5W0ERFRDy4eWYNdeDHY78MNttzx4uVv7Qtvo5I2IiIKuESUVVBxqrX2f5Mgp4Xquewf9/znwC8+KgdS/RORh+ph+nDYqBYUEVHAxV/fDQKH1W+cvjl1BwD2X72Ey/9xN/t+9/mREX5EJW1ERBRw8WsZHBCAW6qNmfjUw6f3WtNvV77ecQ/2X72EoPUdj91oYGZfuG4P2KwWFBFRwMWdGsgGngf6Bqx73W3XTWvYdVPEIt0qaSMi3e0D3BvX3lNTNGfibHv2pPnYsPl4p8XqYl/AgHpYCuwaxeoNwIw6uFGz/ouIqAcX17bCFBNFuFlYbOEXw+BjtZqIxEDv8KMBXRZpRuPg/Lu1Y6NZLwi3KNxEJIZuBb4D5qgpFHDR2i2alWx0pzBFREQBFze+iWYlJ8r1REREARcXLLwaxWqNwJtqLRERBVwiKbWYTa2s82gG/FdNJSKigEsYGfBfgz0f984kPx8F4Cq1lIiIAi7hpMPTBrINvLVteuWtbuA9CBw/BNaplUREJFp74k7X3/QxL9Yb9QNYsDfY3vBX7SIRiSMzwsfJuWqK5jTQOwrffH+q0lFriIgkBp2iFBERBZyIiIgCTkRERAEnIiLSuXSTiYhI4lqIO6vSUjWFAk5EJJm8GH6IAk5EJKnsjVv9ZD2wXM2xPV2DExFJXFcB7wEPqCkUcO21BvgS+FZNISKSGHSKMjrnqglERNSDExERUcAliNuBCuB6NYWISGLQKcro/BA4CVilphARUQ9OREREASciIqKAExERUcCJiIgCTkRERAEnIiKigBMREelWGgcnIpK45gBVwGdqCgWciEgyeSv8EAWciEhSORgYBqwG3lBzbE/X4EREEtf5wFNonlwFnIiI9Bw6RRmdEmAesFRNISKigEsmz6gJREQUcMkoHdgZWAusUHOIiMQ/XYOLzoPAu8CtagoREQWciIiIAk5EREQBJyIiooATEREFnIiIiAJOREREASciItKtNNBbRCRxbZtGcI2aQgEnIpJMKsMP8aFTlCIiiesk4GrgzG54rx2AE4E0BZyIiHS104A7gCnd8F6bgb2B/wIvAwXAkHhuHJ2iFBGRaD0BWOAxYEJ42Re4FVfmAv8A6tSDSyxOeKc6agoR6eGeBM4BQuH/DwLygReADcBrwDXASDXV9vYMB0nTxzw1i4iIrxnh4+TcGLz3WUCjzzHbArXhf1cCDwGTgJ0UcAo4EZFECLjWQm7bowGoD/f43mnSuzMKuPgIuKtxzzlP0/dJRBRw2zkzipDz9u4c4Fvg8XDvrr8CLjYGAJ+Et2U57t1KffS9EhEFXLtDrmnvri7cu/sQKEzm3l28BdwY3BkCvNtUBWTpuyUiMbQT8FL4mPRvYJcYb89P2hlyTR9bwmG3DngaOC8Ofq+kDLj9ccd9RNoRnwE76zsmIjFwLrDWc0zaCOTGeLtODvfKbCc8mvbuPgZuA44jge/+j6eA+3sUO+Bmfc9EpJv9P74fuuT3mBonIed0UtB5e3cbcMfd5YYzI6Jt5zl/ABwWBztuAO4Yi6beBa7r5u3oAzxF6wPhPwcu1PdNRLrx2PQXYMcW1qnFPbW3IYbbeSTu9bQAXXM9zQmHaBqwFHgeKMcdaN7gDbiJuOdyRUREOjOIDF1/00g9EAz38hbgTiX2kgJORES6yku4U3oFuzlUAYym6hIRka4yEPf6WXcJ8f2dnP/UZMsiItJVjqTr73qsBXrh3kW6AHgO95rcum2nKIcT+ztvwK03lO9ZtgJ3PER364d7K27fCM9/jXtDTEifYRHpJjvhTjbRUmhY4E/AdzHczsOB47so3BrDx91U3Ek4nsKd6Pm9bu4ttlm8DfQ+AFjis03luHeeioh0t7to+Xb6B2O8fVfTeWPh/IYI/J0EHQAej1N1pQCnAL8Brg//VSIiEiupwKMRguCvuKfrYmV6J4VbCHeQt0OSDPKO14ATEYlHObilaOYDDwM/ivH2TKdjU3VtpY0DuRVwIiLS1a5qR7g54V6aBWrCvbQcWp9gQwEnIiJxF2514XU3Numl7dVTGkoBJyKSPOHm8H1176a9tNTu2DiNgxMRiU+DgbM9yxbiVsWOB1cCt9N8lpKt4WUh4FXcux7nAl/29B2qHpyIiOtyn+PhuDjquTk0v5ZWHe6lnagOlAJORCSSv9H81vl4qEF5FdtfU5uLW1VlH+0yBZyISDQ+9xwLP4yDbboCd/KLu4Fs3HI1ooATEYnaXj7HwuIYb1NvYL9EakRVExARiT/H+ix7K8bbVAd8poATEZGOOMZn2ZtqlrbRXS6t2xU4EBga7p73xy0bb4HNwLe4M1ovTrS/bkQkYQLuG9w7FCWBxcM1uD2BC3AnLV1B26aeWUzrpSxERFrSi+9vu9/2eE7Noh5cR43GLZhn2vnzI4FZuDXtTgNWqUlFpB3HEW9FgLcjrLszMCj86Ic7BdbnuGeVbE9vSAXc9ka1EG6bcWe5rg3/vz8wIMK6hwOLgMPCPyMiEq3Wrr/9ADgf+ClwCP5njNbgzvV4O7BcTRofYn2K8nm+v7Y2H7gWd6xHpMlAdwPOwZ2Oxu+U5X3apSLSRk95jiP1QF/c2/RvDvfSor1s0oA7I4oo4PgT8LPwh6mtcvl+6pptj3XEtvigiCSerzzHkXdwb3L7kPbXWrtNzaqA66hin+0/QrtVRKK0n88x5GVgNdtPj/U07kTMhwK7h39uPFCCO9mxX8hNVvMq4Dpigs/2n6bdKiJROqeVnthzuDeUtOQgmk/zZYFP6aYyNfFCt7N3ri0+y0JqFhGJ0jEtPPcr4HTgi1Ze4yPg1HBPr6nB4Z9XwEm7HOSzbI2aRUQ6GHA3AHe24XU+AB72WT62JzWmhglENgI4OPxvOu6tuX3Dj50i/MxAz/8t7ngUEZHW9MG97d9rAXBrO17vMeAiz7JD1cyxE+trcCOAe3HPVdtOeGhqHRGJ1vE+x5DNtH8G/zSg0fN6PWpMnHpwrsHhv5DOpnNP2/5TTSsiURrls+xR2j/HbT3uUKWBnl6iAq4HmQA8jv+sJI24xf0+xh2bsib8gfFzOG5V26beUvOKSDsDrjMmi/AWJNXMSjHU3acoJ+De5eh9z/nAWcAObXit231e5zDtUhGJ0mqaT97eEf18jkkL1Mw9I+D2xi1B0fS9aoEz2/l6n3hea5N6yCISpXSfY98dHXzNw31e8/6e1Kg9eZjAxTS/6zEfd4aAtjoCGO5Z9g7uKU4Rkdb4DQ94o4OvOd5n2Wtq6p7Rg1vleZ8vgGA7X+tZn+3+nXaniETpQTp/mr+3PK+3FdhFTZ38AbcL/vO9tccZ+A8ROEW7U0Si9J7PMWRwB17vCJ/Xm61m7hkBNwL/G0vaajjNr+NZ3KoCA7U7RSQK/XDL2niPIxkdeM3ZPq93vJq6ZwTcIJ/3WR/+oEXrIGBlhN7bUu1KEYnS6AjHkZPa+XpjaF66a15PbNieepPJV7i35Da1E+6YE9PKz6YBVwL/CgeyH41/E5FoHdNCULXVjrhzUDY9jm0FLlMzx14v4GTP48gueq/fR/iraRFuiZum8032wR2EeSPwZZN1Q8ALPq9xoXaliETphQjHolW0bSxuP9y7JL2vM11N3PP0A6poeS7Jdbjj2fye2whMihCUB6p5RSQKBv/r+NseTxLd3d0ZNL9r0gJ/o/WzUpKkBuFOxdXWSZTn8/0FYO9fTN+hMkQiEp0sn+PLx57/l+NfigsgE7gFtxal3/0LvdXEPVtv4DfA162E2mbgr8CJTX42BfdGk7VNHn9Xk4pIlM73OdZMxp0f17v8E9yJKP4IvIR7M1uk49UTuKW9enz3WFxpuGNHDset/RbAnVx5DVCDO06lXs0kIp2oBMj1LNs3fNyZjVuZuy02A9cCD6hpRUQklj6k+YxK2wRx79heS+uXTTbgVvzeTU2qHpyISDw4lO2v2W8CKj3r9ANOB44F9gF2xz2b9F/cospluHd/b1VzioiI9AD/HwhYv8UwCQ4qAAAAJXRFWHRkYXRlOmNyZWF0ZQAyMDE3LTA3LTI3VDE2OjUyOjA3KzAwOjAw8bT+LAAAACV0RVh0ZGF0ZTptb2RpZnkAMjAxNy0wNy0yN1QxNjo1MjowNyswMDowMIDpRpAAAAAASUVORK5CYII="}}},{"metadata":{},"cell_type":"markdown","source":"### In our case, the x-axis is the slice location (ImagePositionPatient[2] from dcm meta information), y-axis is the lung-area, applying trapezoidal method give as an approximate of lung volume"},{"metadata":{},"cell_type":"markdown","source":"## Lung Volume Calculus"},{"metadata":{"trusted":true},"cell_type":"code","source":"class Detector:\n    def __call__(self, x):\n        raise NotImplementedError('Abstract') \n        \nclass ThrDetector(Detector):\n    def __init__(self, thr=-400):\n        self.thr = thr\n        \n    def __call__(self, x):\n        try:\n            x = pydicom.dcmread(x)\n            img = x.pixel_array\n            img = (img + x.RescaleIntercept) / x.RescaleSlope\n            img = img < self.thr\n            \n            img = clear_border(img)\n            img = label(img)\n            areas = [r.area for r in regionprops(img)]\n            areas.sort()\n            if len(areas) > 2:\n                for region in regionprops(img):\n                    if region.area < areas[-2]:\n                        for coordinates in region.coords:                \n                            img[coordinates[0], coordinates[1]] = 0\n                            \n            area = (img > 0).sum() * x.PixelSpacing[0] * x.PixelSpacing[1] # scale the detected lung area according the the pixel spacing value\n            \n        except:\n            area = np.nan\n\n        try:\n            loc = x.ImagePositionPatient[2]\n        except:\n            loc = np.nan\n\n        return area, loc\n  \nclass Integral:\n    def __init__(self, detector: Detector):\n        self.detector = detector\n    \n    def __call__(self, xs):\n        raise NotImplementedError('Abstract')\n        \n\nclass AreaIntegral(Integral):\n    def __call__(self, xs):\n        \n        with Pool(4) as p:\n            areas, locs = map(list, zip(*p.map(self.detector, xs) ))\n        \n        filt = (~np.isnan(locs)) & (~np.isnan(areas))\n        areas = np.array(areas)[filt]\n        locs = np.array(locs)[filt]\n        seq_idx = np.argsort(locs)\n\n        return np.trapz(y=areas[seq_idx], x=locs[seq_idx])/1000","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train = pd.read_csv('../input/osic-pulmonary-fibrosis-progression/train.csv')\ntrain_data = {}\nfor p in train.Patient.values:\n    train_data[p] = os.listdir(f'../input/osic-pulmonary-fibrosis-progression/train/{p}/')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"keys = list(train_data.keys()) #[k for k in list(train_data.keys()) if k not in ['ID00011637202177653955184', 'ID00052637202186188008618']]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"integral = AreaIntegral(ThrDetector()) ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"volume = {}\nfor k in tqdm(keys, total=len(keys)):\n    x = []\n    for i in train_data[k]:\n        x.append(f'../input/osic-pulmonary-fibrosis-progression/train/{k}/{i}') \n    volume[k] = integral(x)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for k in tqdm(train.Patient.values):\n    #if k in ['ID00011637202177653955184', 'ID00052637202186188008618']:\n    #    continue\n    train.loc[train.Patient == k,'v'] = volume[k]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(10, 10))\nplt.xlabel('v')\nplt.ylabel('FVC')\nplt.plot(train.v, train.FVC, '.')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train[['v', 'FVC']].corr('spearman')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Some Observations and Conclusions"},{"metadata":{},"cell_type":"markdown","source":"1. As we could see, most of time, when lung volume increases, the FVC increases as well (High spearman\\ranking correlations)\n2. For **straight lines**, they are from the same patient, but the FVC is taken at different timing\n3. However, there are some outliers need to be further investigated"},{"metadata":{"trusted":true},"cell_type":"code","source":"pd.to_pickle(volume, 'patient_lung_volume.pkl') # for readers to integrate with their modeling :D","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}