--- a
+++ b/test_seg_scan_dsb.py
@@ -0,0 +1,121 @@
+import sys
+import lasagne as nn
+import numpy as np
+import theano
+import pathfinder
+import utils
+from configuration import config, set_configuration
+import theano.tensor as T
+import blobs_detection
+import logger
+import time
+import multiprocessing as mp
+import buffering
+
+
+def extract_candidates(predictions_scan, tf_matrix, pid, outputs_path):
+    print 'computing blobs'
+    start_time = time.time()
+    blobs = blobs_detection.blob_dog(predictions_scan[0, 0], min_sigma=1, max_sigma=15, threshold=0.1)
+    print 'blobs computation time:', (time.time() - start_time) / 60.
+    print 'n blobs detected:', blobs.shape[0]
+
+    blobs_original_voxel_coords = []
+    for j in xrange(blobs.shape[0]):
+        blob_j = np.append(blobs[j, :3], [1])
+        blob_j_original = tf_matrix.dot(blob_j)
+        blobs_original_voxel_coords.append(blob_j_original)
+
+    blobs = np.asarray(blobs_original_voxel_coords)
+    print blobs.shape
+    utils.save_pkl(blobs, outputs_path + '/%s.pkl' % pid)
+
+
+jobs = []
+theano.config.warn_float64 = 'raise'
+
+if len(sys.argv) < 2:
+    sys.exit("Usage: test_luna_scan.py <configuration_name>")
+
+config_name = sys.argv[1]
+set_configuration('configs_seg_scan', config_name)
+
+# predictions path
+predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
+outputs_path = predictions_dir + '/%s' % config_name
+utils.auto_make_dir(outputs_path)
+
+# logs
+logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
+sys.stdout = logger.Logger(logs_dir + '/%s.log' % config_name)
+sys.stderr = sys.stdout
+
+# builds model and sets its parameters
+model = config().build_model()
+
+x_shared = nn.utils.shared_empty(dim=len(model.l_in.shape))
+idx_z = T.lscalar('idx_z')
+idx_y = T.lscalar('idx_y')
+idx_x = T.lscalar('idx_x')
+
+window_size = config().window_size
+stride = config().stride
+n_windows = config().n_windows
+
+givens = {}
+givens[model.l_in.input_var] = x_shared
+
+get_predictions_patch = theano.function([],
+                                        nn.layers.get_output(model.l_out, deterministic=True),
+                                        givens=givens,
+                                        on_unused_input='ignore')
+
+data_iterator = config().data_iterator
+
+print
+print 'Data'
+print 'n samples: %d' % data_iterator.nsamples
+
+start_time = time.time()
+for n, (x, lung_mask, tf_matrix, pid) in enumerate(
+        buffering.buffered_gen_threaded(data_iterator.generate(), buffer_size=2)):
+    print '-------------------------------------'
+    print n, pid
+
+    predictions_scan = np.zeros((1, 1, n_windows * stride, n_windows * stride, n_windows * stride))
+
+    for iz in xrange(n_windows):
+        for iy in xrange(n_windows):
+            for ix in xrange(n_windows):
+                start_time_patch = time.time()
+                x_shared.set_value(x[:, :, iz * stride:(iz * stride) + window_size,
+                                   iy * stride:(iy * stride) + window_size,
+                                   ix * stride:(ix * stride) + window_size])
+                predictions_patch = get_predictions_patch()
+
+                predictions_scan[0, 0,
+                iz * stride:(iz + 1) * stride,
+                iy * stride:(iy + 1) * stride,
+                ix * stride:(ix + 1) * stride] = predictions_patch
+
+    if predictions_scan.shape != x.shape:
+        pad_width = (np.asarray(x.shape) - np.asarray(predictions_scan.shape)) / 2
+        pad_width = [(p, p) for p in pad_width]
+        predictions_scan = np.pad(predictions_scan, pad_width=pad_width, mode='constant')
+
+    if lung_mask is not None:
+        predictions_scan *= lung_mask
+
+    print 'saved plot'
+    print 'time since start:', (time.time() - start_time) / 60.
+
+    jobs = [job for job in jobs if job.is_alive]
+    if len(jobs) >= 3:
+        jobs[0].join()
+        del jobs[0]
+    jobs.append(
+        mp.Process(target=extract_candidates, args=(predictions_scan, tf_matrix, pid, outputs_path)))
+    jobs[-1].daemon = True
+    jobs[-1].start()
+
+for job in jobs: job.join()