{"id":"W2080428195","doi":"10.4236/eng.2013.510b047","title":"Cell Segmentation and Tracking in Microfluidic Platform","year":2013,"lang":"en","type":"article","venue":"Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Winnipeg Regional Health Authority; University of Manitoba; University of Winnipeg","funders":"","keywords":"Segmentation; Microfluidics; Scalability; Ellipse; Tracking (education); Artificial intelligence; Computer vision; Image segmentation; Computer science; Process (computing); Trajectory; Active contour model; Scheme (mathematics); Scale-space segmentation; Face (sociological concept); Pattern recognition (psychology); Nanotechnology; Mathematics; Materials science; Geometry; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008845624,0.00005462536,0.0000516141,0.000097763,0.00001237268,0.00008415282,0.0001143327,0.00002235906,0.00002013022],"category_scores_gemma":[0.00001407004,0.00005592257,0.000007511791,0.0001227354,0.000005469863,0.0007059803,0.00004376415,0.00007136352,0.00001563962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002816623,"about_ca_system_score_gemma":0.000004987385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000266084,"about_ca_topic_score_gemma":1.995877e-7,"domain_scores_codex":[0.9995735,0.000004139487,0.0001139863,0.0001124691,0.00008174236,0.0001142021],"domain_scores_gemma":[0.9998145,0.00003235336,0.00001617933,0.00008282011,0.000009946202,0.00004425089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[1.730523e-7,0.00001274736,0.0005598853,0.00004087531,0.000001534259,0.00000558034,0.00104394,0.00005986348,0.8803361,0.0002166172,0.0007965,0.1169262],"study_design_scores_gemma":[0.0002134823,0.00001641337,0.007527409,0.00003094807,6.941494e-7,0.000005364268,0.00004771325,0.0569194,0.9349214,0.0001357348,0.00006952507,0.0001119249],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.13604,0.000317558,0.8631968,0.00005200443,0.00005265413,0.0001137557,7.940199e-8,0.000135018,0.00009213541],"genre_scores_gemma":[0.7263277,0.0000687699,0.2734096,0.0001071929,0.00001436631,0.00003323527,0.000001090804,0.000006083807,0.00003194918],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5902877,"threshold_uncertainty_score":0.2280456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008743494185622716,"score_gpt":0.2249920818531951,"score_spread":0.2162485876675724,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}