[8fb459]: / utils / __pycache__ / metrics.cpython-36.pyc

Download this file

75 lines (72 with data), 5.9 kB

3

eá`÷Ń@sČddlZddlZddlmZmZdddgdddgdfd	d
äZd ddäZd!ddäZ	d"ddäZ
ddäZddäZddäZ
ddäZd#ddäZd$ddäZdddgdddgfddäZdS)%ÚN)┌hd┌dcÚÚÚZRVZLVMZLVcCs┬g}g}g}xĺ|D]Ő}	|||	dg7}tj|â}
d|
|
|	k<tj|â}d|||	k<tj|
ddâ}
tj|ddâ}t|
|â}t|
||dŹ}
|j|â|j|
âqW||||gt|âdť}tj|âS)zGCalculate the Dice Similarity Coefficient and Hausdorff distance. 
    rr)┌voxelspacing)┌DSC┌HD┌TissueClass┌Phase)	┌np┌copy┌cliprr┌append┌len┌pd┌	DataFrame)ZM_gt┌M_predr┌
tissue_labels┌tissue_label_names┌phaseZDiceZ	Hausdorffr
┌labelZgt_labelZ
pred_labelZdiceZ	hausdorff┌outputęr˙Q/tf/Dropbox (Partners HealthCare)/ubuntu/docker/repos/DeepStrain/utils/metrics.py┌get_geometric_metrics	s"




rcCs<tj|âd}t|jâdkr(tj|dâ}||kjâ|}|S)NiŔÚrÚ    )r┌prodr┌shape┌argmax┌sum)┌M┌voxel_spacing_mm┌tissue_labelZ
voxel_vol_cm3┌	volume_mlrrr┌
get_volume_ml&s
r&š═╠╠╠╠╠­?cCst|||dŹ}||}|S)N)r$)r&)r"r#r$Ztissue_density_g_per_mlr%Zmass_grrr┌
get_mass_g-sr(cCs6t|||dŹ}t|||dŹ}|||}|||dfS)N)r$Úd)r&)┌M_ed┌M_esr#r$ZEDV_mlZESV_mlZEFrrr┌get_volumes_ml_and_ef2sr,cCs t|||ddŹ\}}}|||fS)Nr)r$)r,)r*r+r#┌	RV_EDV_ml┌	RV_ESV_ml┌RV_EFrrr┌get_clinical_parameters_rv8sr0cCs0t|||ddŹ\}}}t||ddŹ}||||fS)Nr)r$r)r,r()r*r+r#┌	LV_EDV_ml┌	LV_ESV_ml┌LV_EF┌	LV_mass_grrr┌get_clinical_parameters_lv<sr5cCs>t|||â\}}}t|||â\}}}}	|||||||	dť}
|
S)N)r-r.r/r1r2r3r4)r0r5)r*r+r#r-r.r/r1r2r3r4Zclinical_parametersrrr┌get_clinical_parametersAs
r6cCsX||}tj|â}tjtj||dât|âdâ}tjtj|ââ}||||j|âfS)aCalculate correlation (corr), bias, standard deviation (std), mean absolute error between x and y measures. 
    
    Bias: The bias between the two tests is measured by the mean of the differences. 
    std : The standard deviation (also known as limits of agreement) between the two tests are defined by a 95% 
          prediction interval of a particular value of the difference.
    
    See: https://ncss-wpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Bland-Altman_Plot_and_Analysis.pdf

    rr)r┌mean┌sqrtr!r┌absZcorrwith)┌x┌yZdk┌bias┌stdZmaerrr┌clinical_metrics_statisticsKs


$r>c	CsÍtjgggggggdťâ}t|âtkr4|gt|â}t|âtkrN|gt|â}xrt|||âD]b\}}}|||â\}}	}
}|||â\}}}
}ttj|	d
âtj|dâ|ddůdŹ}
|j	|
ddŹ}q\Wtj
|dd	Ź|_|S)z╔Calculate clinical metrics on data loader function `loading_fn` for subjects in `listSIDs`.
    Assumes end-diastole and end-systole time frame = `end_diastolic_frame_id`, `end_systolic_frame_id`.
    )r-r.r/r1r2r3r4rNr)r#T)┌ignore_index┌	SubjectID)┌namerr)rr┌type┌intr┌zipr6rr r┌Index┌index)┌
loading_fn┌listSIDs┌ED_ids┌ES_ids┌Clinical_params_pred┌
subject_id┌ED_id┌ES_id┌V┌	M_pred_ed┌affine┌zooms┌	M_pred_es┌clinical_params_predrrr┌"get_clinical_metrics_on_dataloader[s

rUc	CsZtjgggggggdťâ}|jâ}t|âtkr<|gt|â}t|âtkrV|gt|â}x¬t|||âD]Ü\}}}|||â\}	}
}}}
|||â\}	}}}}
ttj	|
dâtj	|dâ|
ddůdŹ}ttj	|dâtj	|dâ|
ddůdŹ}|j
|ddŹ}|j
|ddŹ}qdWtj|dd	Ź|_tj|dd	Ź|_t
||â}tj|d
ddd
gdŹjd
d
ddg}|||fS)z╔Calculate clinical metrics on data loader function `loading_fn` for subjects in `listSIDs`.
    Assumes end-diastole and end-systole time frame = `end_diastolic_frame_id`, `end_systolic_frame_id`.
    )r-r.r/r1r2r3r4rNr)r#T)r?r@)rAr<r=ZMAEZcorr)rFrrrr)rrr
rBrCrrDr6rr rrErFr>┌T)rGrHrIrJZClinical_params_gtrKrLrMrNrOr*rPrQrRr+rSZclinical_params_gtrTZstats_dfrrr┌&compare_clinical_metrics_on_dataloaderqs.



$rWc
CsŽtjggggdťâ}xh|D]`}xZ|D]R}|||â\}}	}
}}ttj|	d
âtj|
dâ|ddů|||dŹ}
|j|
ddŹ}q$WqWtjtj|t|ât|ââdd	Ź|_	|S)záCalculate geometric metrics on data loader function `loading_fn` for subjects in `listSIDs`.
    Metrics are calculated for all frames in `listTimeFrames`.
    )rr	r
rrNr)rrrrT)r?r@)rArr)
rrrrr rrE┌repeatrrF)rGrHZlistTimeFramesrrZGeometric_paramsrLZ
time_framerOr"rrQrRZgeometric_metricsrrr┌'compare_geometric_metrics_on_dataloaderĺs


$rY)r)rr')r)rr)rr)┌numpyr┌pandasrZmedpy.metric.binaryrrrr&r(r,r0r5r6r>rUrWrYrrrr┌<module>s





"