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MarcelRosierMarcelRosier
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update: rerun notebooks with latest version, dark figs
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Lines changed: 76 additions & 55 deletions

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‎panoptica/example_spine_matched_instance.ipynb‎

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"name": "stdout",
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"Requirement already satisfied: panoptica in /Users/marcelrosier/opt/anaconda3/envs/brainles/lib/python3.10/site-packages (from -r requirements.txt (line 1)) (0.5.2)\n",
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"Requirement already satisfied: panoptica in /Users/marcelrosier/opt/anaconda3/envs/brainles/lib/python3.10/site-packages (from -r requirements.txt (line 1)) (0.5.11)\n",
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"Requirement already satisfied: auxiliary in /Users/marcelrosier/opt/anaconda3/envs/brainles/lib/python3.10/site-packages (from -r requirements.txt (line 2)) (0.0.35)\n",
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"Requirement already satisfied: rich in /Users/marcelrosier/opt/anaconda3/envs/brainles/lib/python3.10/site-packages (from -r requirements.txt (line 3)) (13.6.0)\n",
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"Requirement already satisfied: numpy in /Users/marcelrosier/opt/anaconda3/envs/brainles/lib/python3.10/site-packages (from -r requirements.txt (line 4)) (1.25.2)\n",
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"To demonstrate we use a reference and predicition of spine a segmentation with matched instances.\n",
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"\n",
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"\n",
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"<img src=\"./spine_seg/matched_instance/fig.png\" alt=\"matched_instance_figure\" height=\"300\"/>\n"
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"<img src=\"./spine_seg/matched_instance/fig_dark.png\" alt=\"matched_instance_figure\" height=\"300\"/>\n"
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{
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"name": "stdout",
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"text": [
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"evaluate took 3.041011333465576 seconds to execute.\n"
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"evaluate took 2.727099895477295 seconds to execute.\n"
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}
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"False Positives (fp): 3\n",
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"False Negatives (fn): 3\n",
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"Recognition Quality / F1 Score (RQ): 0.8636363636363636\n",
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"Segmentation Quality (SQ): 0.8328184295330796 ± 0.15186064004517466\n",
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"Panoptic Quality (PQ): 0.719252280051296\n",
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"DSC-based Segmentation Quality (DQ_DSC): 0.900292616009954 ± 0.1025356617495733\n",
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"Segmentation Quality (SQ): 0.8328184295330797 ± 0.1518606400451747\n",
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"Panoptic Quality (PQ): 0.7192522800512962\n",
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"DSC-based Segmentation Quality (DQ_DSC): 0.900292616009954 ± 0.10253566174957332\n",
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"DSC-based Panoptic Quality (PQ_DSC): 0.7775254410995057\n",
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"Average symmetric surface distance (ASSD): 0.25033188787922506 ± 0.07696680402317077\n"
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"Average symmetric surface distance (ASSD): 0.250331887879225 ± 0.07696680402317076\n"
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{
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"data": {
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"<span style=\"font-weight: bold\">}</span>\n",
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"</pre>\n"
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‎panoptica/example_spine_semantic.ipynb‎

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"execution_count": 1,
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{
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"Requirement already satisfied: panoptica in /Users/marcelrosier/opt/anaconda3/envs/brainles/lib/python3.10/site-packages (from -r requirements.txt (line 1)) (0.5.2)\n",
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"Requirement already satisfied: panoptica in /Users/marcelrosier/opt/anaconda3/envs/brainles/lib/python3.10/site-packages (from -r requirements.txt (line 1)) (0.5.11)\n",
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"To demonstrate we use a reference and predicition of spine a segmentation without instances.\n",
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"<img src=\"./spine_seg/semantic/fig.png\" alt=\"matched_instance_figure\" height=\"300\"/>"
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"<img src=\"./spine_seg/semantic/fig_dark.png\" alt=\"matched_instance_figure\" height=\"300\"/>"
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"evaluate took 16.80208992958069 seconds to execute.\n"
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"Panoptic: Start Evaluation\n",
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"-- Cropped from (512, 512, 17) to (170, 512, 17)\n",
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"-- Got SemanticPair, will approximate instances\n",
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"-- Set dtype to <class 'numpy.uint8'>\n",
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"-- approximate_instances took 0.03729510307312012 seconds to execute.\n",
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"-- Got UnmatchedInstancePair, will match instances\n",
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"-- match_instances took 0.6606709957122803 seconds to execute.\n",
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"-- Got MatchedInstancePair, will evaluate instances\n",
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"-- evaluate_matched_instance took 3.3390767574310303 seconds to execute.\n",
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"-- evaluate took 4.071817874908447 seconds to execute.\n"
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"Recognition Quality / F1 Score (RQ): 0.8295454545454546\n",
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"Segmentation Quality (SQ): 0.7940127477906024 ± 0.11547745015679488\n",
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"Panoptic Quality (PQ): 0.6586696657808406\n",
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"DSC-based Segmentation Quality (DQ_DSC): 0.8802182546605446 ± 0.07728416427007166\n",
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"DSC-based Segmentation Quality (DQ_DSC): 0.8802182546605446 ± 0.07728416427007168\n",
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"DSC-based Panoptic Quality (PQ_DSC): 0.7301810521615881\n",
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"Average symmetric surface distance (ASSD): 0.20573710924944655 ± 0.1398348236766068\n"
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"Average symmetric surface distance (ASSD): 0.2057371092494465 ± 0.1398348236766068\n",
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"ASSD-based Panoptic Quality (PQ_ASSD): 0.17066828380919993\n"
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{
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" <span style=\"color: #008000; text-decoration-color: #008000\">'sq_sd'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.11547745015679488</span>,\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'pq'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.6586696657808406</span>,\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'sq_dsc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.8802182546605446</span>,\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'sq_dsc_sd'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.07728416427007166</span>,\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'sq_dsc_sd'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.07728416427007168</span>,\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'pq_dsc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.7301810521615881</span>,\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'assd'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.20573710924944655</span>,\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'assd_sd'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.1398348236766068</span>\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'sq_assd'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.2057371092494465</span>,\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'sq_assd_sd'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.1398348236766068</span>,\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'pq_assd'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.17066828380919993</span>\n",
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"<span style=\"font-weight: bold\">}</span>\n",
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"</pre>\n"
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