ENH: Combine 2 paps after postprocessing

ml4h/arguments.py

@@ -386,7 +386,7 @@ def parse_args():
 
     # Arguments for explorations/infer_stats_from_segmented_regions
     parser.add_argument('--analyze_ground_truth', default=False, action='store_true', help='Whether or not to filter by images with ground truth segmentations, for comparison')
-    parser.add_argument('--structures_to_analyze', nargs='*', default=[], help='Structure names to include in the .tsv files and scatter plots. Must be in the same order as the output channel map.')
+    parser.add_argument('--structures_to_analyze', nargs='*', default=[], help='Structure names to include in the .tsv files and scatter plots. Must be in the same order as the output channel map. Use + to merge structures before postprocessing, and ++ to merge structures after postprocessing.')
     parser.add_argument('--erosion_radius', nargs='*', default=[], type=int, help='Radius of the unit disk structuring element for erosion preprocessing, optionally as a list per structure to analyze')
     parser.add_argument('--intensity_thresh', type=float, help='Threshold value for preprocessing')
     parser.add_argument('--intensity_thresh_in_structures', nargs='*', default=[], help='Structure names whose pixels should be replaced if the images has intensity above the threshold')

ml4h/explorations.py

@@ -790,13 +790,12 @@ def _intensity_thresh_auto(
         return (bins[np.where(pred == 1)[0][-1]][0] + bins[np.where(pred == 0)[0][0]][0]) / 2
 
 def _scatter_plots_from_segmented_region_stats(
-    inference_tsv_true, inference_tsv_pred, structures_to_analyze,
-    output_folder, id, input_name, output_name,
+    inference_tsv_true, inference_tsv_pred, output_folder, id, input_name, output_name,
 ):
     df_true = pd.read_csv(inference_tsv_true, delimiter='\t', quotechar='"', quoting=csv.QUOTE_MINIMAL)
     df_pred = pd.read_csv(inference_tsv_pred, delimiter='\t', quotechar='"', quoting=csv.QUOTE_MINIMAL)
 
-    results_to_plot = [f'{s}_median' for s in structures_to_analyze]
+    results_to_plot = [c for c in df_pred.columns if 'median' in c]
     for col in results_to_plot:
         for i in ['all', 'filter_outliers']: # Two types of plots
             plot_data = pd.concat(
@@ -864,28 +863,54 @@ def infer_stats_from_segmented_regions(args):
     _, _, generate_test = test_train_valid_tensor_generators(**args.__dict__)
     model, _, _, _ = make_multimodal_multitask_model(**args.__dict__)
 
-    # the user can use '+' to create a new channel that merges other channels
-    # those channels must be included alone in structures_to_analyze as well, and must be at the end of the list
-    merged_locs = [k for k in range(len(args.structures_to_analyze)) if '+' in args.structures_to_analyze[k]]
+    # the user can use '+' to create a new channel that merges other channels before postprocessing
+    # the user can use '++' to create a new channel that merges other channels after postprocessing
+    # those channels must be included alone in structures_to_analyze as well, then '+', then '++'
+    merged_locs = [
+        k for k in range(len(args.structures_to_analyze))
+        if '+' in args.structures_to_analyze[k] and '++' not in args.structures_to_analyze[k]
+    ]
+    merged_after_locs = [
+        k for k in range(len(args.structures_to_analyze))
+        if '++' in args.structures_to_analyze[k]
+    ]
     uni_locs = [k for k in range(len(args.structures_to_analyze)) if '+' not in args.structures_to_analyze[k]]
     assert((len(merged_locs) == 0) or (merged_locs[0] > uni_locs[-1]))
+    assert((len(merged_after_locs) == 0) or (merged_after_locs[0] > uni_locs[-1]))
     merged_structures = [args.structures_to_analyze[k] for k in merged_locs]
+    merged_after_structures = [args.structures_to_analyze[k] for k in merged_after_locs]
     uni_structures = [args.structures_to_analyze[k] for k in uni_locs]
     merged_channels = [k.split('+') for k in merged_structures]
+    merged_after_channels = [k.split('++') for k in merged_after_structures]
     for i in range(len(merged_channels)):
         for j in range(len(merged_channels[i])):
             merged_channels[i][j] = tm_out.channel_map[merged_channels[i][j]]
+    for i in range(len(merged_after_channels)):
+        for j in range(len(merged_after_channels[i])):
+            merged_after_channels[i][j] = tm_out.channel_map[merged_after_channels[i][j]]
 
     # structures have to be in the same order as the channel map
     good_channels = [tm_out.channel_map[k] for k in uni_structures]
     assert (good_channels == sorted(good_channels))
-    good_structures = [[k for k in tm_out.channel_map.keys() if tm_out.channel_map[k] == v][0] for v in good_channels] + merged_structures
+    title_structures = [[k for k in tm_out.channel_map.keys() if tm_out.channel_map[k] == v][0] for v in good_channels] \
+                       + merged_structures + merged_after_structures
     nb_orig_channels = len(tm_out.channel_map)
     nb_out_channels = len(good_channels) + len(merged_structures)
     bad_channels = [k for k in range(nb_orig_channels) if k not in good_channels]
     for m in merged_channels:
         for c in m:
             assert(c in good_channels)
+    for m in merged_after_channels:
+        for c in m:
+            assert(c in good_channels)
+
+    # TODO take me out
+    print(args.structures_to_analyze)
+    print(merged_structures)
+    print(merged_channels)
+    print(merged_after_structures)
+    print(merged_after_channels)
+    assert(False)
 
     # Structuring element used for the erosion
     if len(args.erosion_radius) > 0:
@@ -933,11 +958,11 @@ def infer_stats_from_segmented_regions(args):
         inference_writer_pred = csv.writer(inference_file_pred, delimiter='\t', quotechar='"', quoting=csv.QUOTE_MINIMAL)
 
         header = ['sample_id']
-        header += [f'{k}_mean' for k in good_structures]
-        header += [f'{k}_median' for k in good_structures]
-        header += [f'{k}_std' for k in good_structures]
-        header += [f'{k}_iqr' for k in good_structures]
-        header += [f'{k}_count' for k in good_structures]
+        header += [f'{k}_mean' for k in title_structures]
+        header += [f'{k}_median' for k in title_structures]
+        header += [f'{k}_std' for k in title_structures]
+        header += [f'{k}_iqr' for k in title_structures]
+        header += [f'{k}_count' for k in title_structures]
         header += ['mri_date']
         inference_writer_true.writerow(header)
         inference_writer_pred.writerow(header)
@@ -1025,8 +1050,7 @@ def postprocess_seg_and_write_stats(y, inference_writer):
     # Scatter plots
     if args.analyze_ground_truth:
         _scatter_plots_from_segmented_region_stats(
-            inference_tsv_true, inference_tsv_pred, args.structures_to_analyze,
-            args.output_folder, args.id, tm_in.input_name(), tm_out.output_name(),
+            inference_tsv_true, inference_tsv_pred, args.output_folder, args.id, tm_in.input_name(), tm_out.output_name(),
         )
 
     # pngs