Merge pull request #43 from sct-pipeline/refactor_stat

Refactoring of statistics

README.md

@@ -7,29 +7,6 @@ Evaluate the sensitivity of atrophy detection with SCT. The algorithm works as f
 - Segment the cord
 - Compute CSA --> CSA(sI, rX, tY)
 
-After everything is done, compute stats:
-- intra-subject MEAN: MEAN[CSA(sI, rX, :)] --> MEAN_intra(sI, rX)
-- intra-subject STD (per subject): STD[CSA(sI, rX, :)] --> STD_intra_sub(sI, rX)
-- intra-subject COV (per subject): STD[CSA(sI, rX, :)] / MEAN[CSA(sI, rX, :)] --> COV_intra_sub(sI, rX)
-- intra-subject STD: MEAN[STD_intra_sub(:, rX)] --> STD_intra(rX)
-- intra-subject COV: MEAN[COV_intra_sub(:, rX)] --> COV_intra(rX)
-- inter-subject STD: STD[MEAN_intra(:, rX)] --> STD_inter(rX)
-
-- rescale_estimated_subject MEAN: MEAN[CSA(sI, rX, :)] / MEAN[CSA(sI, 1, :)] --> MEAN_rescale_estimated_subject(sI, rX)
-- rescale_estimated (across subjects) MEAN: MEAN[MEAN_rescale_estimated_subject(:, rX)] --> MEAN_rescale_estimated(rX)
-- rescale_estimated (across subjects) STD: STD[MEAN_rescale_estimated_subject(:, rX)] --> STD_rescale_estimated(rX)
-
-- error (across subjects) MEAN: MEAN[MEAN_error_intra(:, rX)]
-- error (across subjects) STD: STD[MEAN_error_intra(:, rX)]
-- sample size: [(z(uncertainty) + z(power))^2 * (2 * STD[MEAN(:, rX)]^2)] / [MEAN[CSA(sI, 1, :)] - MEAN[CSA(sI, rX, :)]] 
-
-Plot results:
-- STD_intersub
-- Mean and STD inter-subject error percentage in function of rescaling
-- sample size: minimum number of patients to detect an atrophy of X with Y% power and Z% uncertainty
-- CSA values boxplot in function of rescaling
-- Error values boxplot in function of rescaling
-
 # How to run
 
 This code has been tested using Python 3.7.
@@ -63,7 +40,7 @@ sct_run_batch -config config_sct_run_batch.yml
 
 To output statistics, run in Dataset
 ~~~
-csa_rescale_stat -i csa_atrophy_results/results -o csa_atrophy_results -config config_script.yml -v
+csa_rescale_stat -i csa_atrophy_results/results -o csa_atrophy_results -config config_script.yml -fig
 ~~~
 
 # Quality Control
@@ -82,3 +59,34 @@ are present in the derivatives/ folder of the dataset, otherwise labeling will b
 ~~~
 sct_run_batch -config config_sct_run_batch.yml
 ~~~
+
+# Statistics
+
+After everything is done, compute stats:
+Per-subject stat: Panda dataframe `df_sub`:
+- intra-subject MEAN: MEAN[CSA(sI, rX, :)] --> MEAN_intra(sI, rX): `df_sub['mean']`
+- intra-subject STD: STD[CSA(sI, rX, :)] --> STD_intra(sI, rX): `df_sub['std']`
+- intra-subject COV: STD[CSA(sI, rX, :)] / MEAN[CSA(sI, rX, :)] --> COV_intra(sI, rX): `df_sub['cov']`
+- rescale_estimated_subject MEAN: MEAN[CSA(sI, rX, :) / CSA(sI, 1, :)] --> MEAN_rescale_estimated_subject(sI, rX): `df_sub['rescale_estimated']`
+- intra-subject error MEAN: MEAN[CSA(sI, rX, :)] - (rX^2 * MEAN[CSA(sI, 1, :)]) --> MEAN_error_intra(sI, rX): `df_sub['error']`
+- intra-subject error in percentage MEAN: [MEAN[CSA(sI, rX, :)] - (rX^2 * MEAN[CSA(sI, 1, :)])] / MEAN[CSA(sI, rX, :)] --> MEAN_error_intra_perc(sI, rX): `df_sub['perc_error']`
+
+Across-subject stats: Panda dataframe `df_rescale`
+- intra-subject STD: MEAN[STD_intra(:, rX)] --> STD_intra(rX): `df_rescale['std_intra']`
+- intra-subject COV: MEAN[COV_intra_sub(:, rX)] --> COV_intra(rX): `df_rescale['cov']`
+- inter-subject STD: STD[MEAN_intra(:, rX)] --> STD_inter(rX): `df_rescale['std_inter']`
+- rescale_estimated (across subjects) MEAN: MEAN[MEAN_rescale_estimated_subject(:, rX)] --> MEAN_rescale_estimated(rX): `df_rescale['mean_rescale_estimated']`
+- rescale_estimated (across subjects) STD: STD[MEAN_rescale_estimated_subject(:, rX)] --> STD_rescale_estimated(rX): `df_rescale['std_rescale_estimated']`
+- error in percentage (across subjects) MEAN: MEAN[MEAN_error_intra(:, rX)]
+- error in percentage (across subjects) STD: STD[MEAN_error_intra(:, rX)]
+
+Power analysis:
+- sample size: [(z(uncertainty) + z(power))^2 * (2 * STD[MEAN(:, rX)]^2)] / [MEAN[CSA(sI, 1, :)] - MEAN[CSA(sI, rX, :)]] 
+
+Plot results:
+- STD_intersub
+- Mean and STD inter-subject error percentage in function of rescaling
+- sample size: minimum number of patients to detect an atrophy of X with Y% power and Z% uncertainty
+- CSA values boxplot in function of rescaling
+- Error values boxplot in function of rescaling
+

config_correction.yml

@@ -0,0 +1,6 @@
+FILES_SEG:
+- sub-amu01_T1w.nii.gz
+FILES_GMSEG:
+
+FILES_LABEL:
+- sub-amu01_T1w.nii.gz

config_script.yml

@@ -26,6 +26,14 @@ stats:
     atrophy_sample: 7.7
     conf: 0.95
     power: 0.8
+fig:
+  sample_size:
+    atrophy_sample: 7.7
+    conf: 1.96
+    power:
+    - 0.84
+    - 1.282
+
 
 # affine_transfo.py
 # -----------------