diff --git a/caiman/base/rois.py b/caiman/base/rois.py
index e268aeeff..a3671795f 100644
--- a/caiman/base/rois.py
+++ b/caiman/base/rois.py
@@ -131,9 +131,9 @@ def extract_binary_masks_from_structural_channel(Y,
     for i in range(areas[1]):
         temp = (areas[0] == i + 1)
         if expand_method == 'dilation':
-            temp = dilation(temp, selem=selem)
+            temp = dilation(temp, footprint=selem)
         elif expand_method == 'closing':
-            temp = closing(temp, selem=selem)
+            temp = closing(temp, footprint=selem)
 
         A[:, i] = temp.flatten('F')
 
diff --git a/caiman/source_extraction/cnmf/initialization.py b/caiman/source_extraction/cnmf/initialization.py
index df734561e..8aa26cbbd 100644
--- a/caiman/source_extraction/cnmf/initialization.py
+++ b/caiman/source_extraction/cnmf/initialization.py
@@ -444,6 +444,10 @@ def initialize_components(Y, K=30, gSig=[5, 5], gSiz=None, ssub=1, tsub=1, nIter
 
         f_in = resize(np.atleast_2d(f_in), [b_in.shape[-1], T])
 
+    elif nb == 0:
+        b_in = np.empty((np.prod(d), 0), dtype=np.float32, order='F')
+        f_in = np.empty((0, T), dtype=np.float32)
+
     if Ain.size > 0:
         Cin = resize(Cin, [K, T])
         center = np.asarray(
@@ -970,11 +974,13 @@ def onclick(event):
 
     res = np.reshape(Y, (np.prod(d[0:-1]), d[-1]),
                      order='F') + med.flatten(order='F')[:, None]
-#    model = NMF(n_components=nb, init='random', random_state=0)
-    model = NMF(n_components=nb, init='nndsvdar')
-    b_in = model.fit_transform(np.maximum(res, 0)).astype(np.float32)
-    f_in = model.components_.astype(np.float32)
-
+    if nb > 0:
+        model = NMF(n_components=nb, init='nndsvdar')
+        b_in = model.fit_transform(np.maximum(res, 0)).astype(np.float32)
+        f_in = model.components_.astype(np.float32)
+    else:
+        b_in = np.empty((A.shape[0], 0), dtype=np.float32, order='F')
+        f_in = np.empty((0, C.shape[1]), dtype=np.float32)
     return A, C, np.array(center, dtype='uint16'), b_in, f_in
 
 #%%
@@ -1140,7 +1146,7 @@ def hals(Y, A, C, b, f, bSiz=3, maxIter=5):
     else:
         ind_A = A>1e-10
 
-    ind_A = spr.csc_matrix(ind_A)  # indicator of nonnero pixels
+    ind_A = spr.csc_matrix(ind_A)  # indicator of nonzero pixels
 
     def HALS4activity(Yr, A, C, iters=2):
         U = A.T.dot(Yr)
@@ -1166,13 +1172,16 @@ def HALS4shape(Yr, A, C, iters=2):
         return A
 
     Ab = np.c_[A, b]
-    Cf = np.r_[C, f.reshape(nb, -1)]
+    Cf = np.r_[C, f]
     for _ in range(maxIter):
         Cf = HALS4activity(np.reshape(
             Y, (np.prod(dims), T), order='F'), Ab, Cf)
         Ab = HALS4shape(np.reshape(Y, (np.prod(dims), T), order='F'), Ab, Cf)
 
-    return Ab[:, :-nb], Cf[:-nb], Ab[:, -nb:], Cf[-nb:].reshape(nb, -1)
+    if nb == 0:
+        return Ab, Cf, b, f
+    else:
+        return Ab[:, :-nb], Cf[:-nb], Ab[:, -nb:], Cf[-nb:].reshape(nb, -1)
 
 
 @profile
@@ -1385,7 +1394,7 @@ def compute_B(b0, W, B):  # actually computes -B to efficiently compute Y-B in p
             b_in, s_in, f_in = spr.linalg.svds(B, k=nb)
             f_in *= s_in[:, np.newaxis]
     else:
-        b_in = np.empty((A.shape[0], 0))
+        b_in = np.empty((A.shape[0], 0), order='F')
         f_in = np.empty((0, T))
         if nb == 0:
             logging.info('Returning background as b0 and W')
diff --git a/caiman/source_extraction/cnmf/spatial.py b/caiman/source_extraction/cnmf/spatial.py
index dc8a0d7ef..29809fa66 100644
--- a/caiman/source_extraction/cnmf/spatial.py
+++ b/caiman/source_extraction/cnmf/spatial.py
@@ -1050,22 +1050,24 @@ def computing_indicator(Y, A_in, b, C, f, nb, method, dims, min_size, max_size,
             px = (np.sum(dist_indicator, axis=1) > 0)
             not_px = ~px
 
-            if nb>1:
-                    f = NMF(nb, init='nndsvda').fit(np.maximum(Y[not_px, :], 0)).components_
-            else:
-                if Y.shape[-1] < 30000:
-                    f = Y[not_px, :].mean(0)
+            if nb > 0:
+                if nb > 1:
+                        f = NMF(nb, init='nndsvda').fit(np.maximum(Y[not_px, :], 0)).components_
                 else:
-                    print('estimating f')
-                    f = 0
-                    for xxx in np.where(not_px)[0]:
-                        f += Y[xxx]
-                    f /= not_px.sum()
-
-            f = np.atleast_2d(f)
-
-            Y_resf = np.dot(Y, f.T)
-            b = np.maximum(Y_resf, 0) / (np.linalg.norm(f)**2)
+                    if Y.shape[-1] < 30000:
+                        f = Y[not_px, :].mean(0)
+                    else:
+                        print('estimating f')
+                        f = 0
+                        for xxx in np.where(not_px)[0]:
+                            f += Y[xxx]
+                        f /= not_px.sum()
+                f = np.atleast_2d(f)
+                Y_resf = np.dot(Y, f.T)
+                b = np.maximum(Y_resf, 0) / (np.linalg.norm(f)**2)
+            else:
+                f = np.empty((0, Y.shape[-1]), dtype='float32')
+                b = np.empty((Y.shape[0], 0), dtype='float32')
             C = np.maximum(csr_matrix(dist_indicator_av.T).dot(
                 Y) - dist_indicator_av.T.dot(b).dot(f), 0)
             A_in = scipy.sparse.coo_matrix(A_in.astype(np.float32))
diff --git a/caiman/source_extraction/cnmf/temporal.py b/caiman/source_extraction/cnmf/temporal.py
index 1f03be530..a1c9973b2 100644
--- a/caiman/source_extraction/cnmf/temporal.py
+++ b/caiman/source_extraction/cnmf/temporal.py
@@ -197,7 +197,8 @@ def update_temporal_components(Y, A, b, Cin, fin, bl=None, c1=None, g=None, sn=N
 
     A = scipy.sparse.hstack((A, b)).tocsc()
     S = np.zeros(np.shape(Cin))
-    Cin = np.vstack((Cin, fin))
+    if fin is not None:
+        Cin = np.vstack((Cin, fin))
     C = Cin.copy()
     nA = np.ravel(A.power(2).sum(axis=0)) + np.finfo(np.float32).eps