diff --git a/README.md b/README.md
index 1a8da07..58aedeb 100755
--- a/README.md
+++ b/README.md
@@ -467,7 +467,9 @@ Of note: Meshing is a memory intensive operation. The underlying zmesh library h
 Igneous provides the engine for performing out-of-core skeletonization of labeled images. 
 The in-core part of the algorithm is provided by the [Kimimaro](https://github.com/seung-lab/kimimaro) library.  
 
-The strategy is to apply Kimimaro mass skeletonization to 1 voxel overlapping chunks of the segmentation and then fuse them in a second pass. Both sharded and unsharded formats are supported. For very large datasets, note that sharded runs better on a local cluster as it can make use of `mmap`.
+The strategy is to apply Kimimaro mass skeletonization to 1 voxel overlapping chunks of the segmentation and then fuse them in a second pass. Both sharded and unsharded formats are supported. For very large datasets, note that sharded runs better on a local cluster as it can make use of `mmap`.  
+
+We also support computing the cross sectional area at each vertex, but be aware that this will add significant time to the computation (currently many hours for a densely labeled task). This is practical for sparse labeling though. This should be improved substantially in the future.
 
 #### CLI Skeletonization
 
@@ -513,6 +515,8 @@ tasks = tc.create_skeletonizing_tasks(
     parallel=1, # Number of parallel processes to use (more useful locally)
     spatial_index=True, # generate a spatial index for querying skeletons by bounding box
     sharded=False, # generate intermediate shard fragments for later processing into sharded format
+    cross_sectional_area=False, # Compute the cross sectional area for each vertex.
+    cross_sectional_area_smoothing_window=5, # Rolling average of vertices.
   )
 
 # Second Pass: Fuse Skeletons (unsharded version)
diff --git a/igneous/task_creation/skeleton.py b/igneous/task_creation/skeleton.py
index 0d2c428..a328d33 100644
--- a/igneous/task_creation/skeleton.py
+++ b/igneous/task_creation/skeleton.py
@@ -49,7 +49,9 @@ def create_skeletonizing_tasks(
     parallel=1, fill_missing=False, 
     sharded=False, frag_path=None, spatial_index=True,
     synapses=None, num_synapses=None,
-    dust_global=False
+    dust_global=False, 
+    cross_sectional_area=False,
+    cross_sectional_area_smoothing_window=5,
   ):
   """
   Assign tasks with one voxel overlap in a regular grid 
@@ -124,6 +126,13 @@ def create_skeletonizing_tasks(
     Iterable yielding ((x,y,z),segid,swc_label)
 
   num_synapses: If synapses is an iterator, you must provide the total number of synapses.
+
+  cross_sectional_area: At each vertex, compute the area covered by a 
+  section plane whose direction is defined by the normal vector pointing
+  to the next vertex in the sequence. (n.b. this will add significant time
+  to the total computation.)
+  cross_sectional_area_smoothing_window: Perform a rolling average of the 
+    normal vectors across these many vectors.
   """
   shape = Vec(*shape)
   vol = CloudVolume(cloudpath, mip=mip, info=info)
@@ -152,7 +161,18 @@ def create_skeletonizing_tasks(
     vol.skeleton.meta.info['spatial_index']['chunk_size'] = tuple(shape * vol.resolution)
   
   vol.skeleton.meta.info['mip'] = int(mip)
-  vol.skeleton.meta.info['vertex_attributes'] = vol.skeleton.meta.info['vertex_attributes'][:1]
+  vol.skeleton.meta.info['vertex_attributes'] = [ 
+    attr for attr in vol.skeleton.meta.info['vertex_attributes']
+    if attr["data_type"] == "float32"
+  ]
+
+  if cross_sectional_area:
+    vol.skeleton.meta.info['vertex_attributes'].append({
+      "id": "cross_sectional_area",
+      "data_type": "float32",
+      "num_components": 1,
+    })
+
   vol.skeleton.meta.commit_info()
 
   will_postprocess = bool(np.any(vol.bounds.size3() > shape))
@@ -187,6 +207,8 @@ def task(self, shape, offset):
         spatial_grid_shape=shape.clone(), # used for writing index filenames
         synapses=bbox_synapses,
         dust_global=dust_global,
+        cross_sectional_area=bool(cross_sectional_area),
+        cross_sectional_area_smoothing_window=int(cross_sectional_area_smoothing_window),
       )
 
     def synapses_for_bbox(self, shape, offset):
@@ -230,6 +252,8 @@ def on_finish(self):
           'spatial_index': bool(spatial_index),
           'synapses': bool(synapses),
           'dust_global': bool(dust_global),
+          'cross_sectional_area': bool(cross_sectional_area),
+          'cross_sectional_area_smoothing_window': int(cross_sectional_area_smoothing_window),
         },
         'by': operator_contact(),
         'date': strftime('%Y-%m-%d %H:%M %Z'),
diff --git a/igneous/tasks/skeleton.py b/igneous/tasks/skeleton.py
index 898bc2f..c2e5d65 100644
--- a/igneous/tasks/skeleton.py
+++ b/igneous/tasks/skeleton.py
@@ -72,9 +72,11 @@ def __init__(
     dust_threshold=1000, progress=False,
     parallel=1, fill_missing=False, sharded=False,
     frag_path=None, spatial_index=True, spatial_grid_shape=None,
-    synapses=None, dust_global=False
+    synapses=None, dust_global=False,
+    cross_sectional_area=False,
+    cross_sectional_area_smoothing_window=1,
   ):
-    super(SkeletonTask, self).__init__(
+    super().__init__(
       cloudpath, shape, offset, mip, 
       teasar_params, will_postprocess, 
       info, object_ids, mask_ids,
@@ -82,7 +84,8 @@ def __init__(
       fix_avocados, fill_holes,
       dust_threshold, progress, parallel,
       fill_missing, bool(sharded), frag_path, bool(spatial_index),
-      spatial_grid_shape, synapses, bool(dust_global)
+      spatial_grid_shape, synapses, bool(dust_global),
+      bool(cross_sectional_area), int(cross_sectional_area_smoothing_window),
     )
     self.bounds = Bbox(offset, Vec(*shape) + Vec(*offset))
     self.index_bounds = Bbox(offset, Vec(*spatial_grid_shape) + Vec(*offset))
@@ -129,7 +132,15 @@ def execute(self):
       fill_holes=self.fill_holes,
       parallel=self.parallel,
       extra_targets_after=extra_targets_after.keys(),
-    )    
+    )
+
+    if self.cross_sectional_area:
+      skeletons = kimimaro.cross_sectional_area(
+        all_labels, skeletons,
+        anisotropy=vol.resolution,
+        smoothing_window=self.cross_sectional_area_smoothing_window,
+        progress=self.progress,
+      )
 
     # voxel centered (+0.5) and uses more accurate bounding box from mip 0
     corrected_offset = (bbox.minpt.astype(np.float32) - vol.meta.voxel_offset(self.mip) + 0.5) * vol.meta.resolution(self.mip)
diff --git a/igneous_cli/cli.py b/igneous_cli/cli.py
index fe62513..2f3521b 100644
--- a/igneous_cli/cli.py
+++ b/igneous_cli/cli.py
@@ -1164,13 +1164,15 @@ def skeletongroup():
 @click.option('--max-paths', default=None, help="Abort skeletonizing an object after this many paths have been traced.", type=float)
 @click.option('--sharded', is_flag=True, default=False, help="Generate shard fragments instead of outputing skeleton fragments.", show_default=True)
 @click.option('--labels', type=TupleN(), default=None, help="Skeletonize only this comma separated list of labels.", show_default=True)
+@click.option('--cross-section', type=int, default=0, help="Compute the cross sectional area for each skeleton vertex. May add substantial computation time. Integer value is the normal vector rolling average smoothing window over vertices. 0 means off.", show_default=True)
 @click.pass_context
 def skeleton_forge(
   ctx, path, queue, mip, shape, 
   fill_missing, dust_threshold, dust_global, spatial_index,
   fix_branching, fix_borders, fix_avocados, 
   fill_holes, scale, const, soma_detect, soma_accept,
-  soma_scale, soma_const, max_paths, sharded, labels
+  soma_scale, soma_const, max_paths, sharded, labels,
+  cross_section,
 ):
   """
   (1) Synthesize skeletons from segmentation cutouts.
@@ -1213,6 +1215,8 @@ def skeleton_forge(
     parallel=1, fill_missing=fill_missing, 
     sharded=sharded, spatial_index=spatial_index,
     dust_global=dust_global, object_ids=labels,
+    cross_sectional_area=(cross_section > 0),
+    cross_sectional_area_smoothing_window=int(cross_section),
   )
 
   enqueue_tasks(ctx, queue, tasks)
diff --git a/requirements.txt b/requirements.txt
index 6880fa0..0a19b5c 100755
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,13 +1,13 @@
 click>=6.7
 cloud-files>=4.11.0
-cloud-volume>=8.11.1
+cloud-volume>=8.29.0
 crackle-codec
 connected-components-3d>=3.10.1
 DracoPy>=1.0.1,<2.0.0
 fastremap>=1.13.2
 google-cloud-logging
-kimimaro>=0.5.0
-mapbuffer>=0.6.0
+kimimaro>=3.4.0
+mapbuffer>=0.7.1
 networkx
 numpy>=1.14.2
 pathos
@@ -20,5 +20,6 @@ shard-computer
 tinybrain
 task-queue>=2.4.0
 tqdm
-zmesh>=1.4,<2.0
-trimesh[easy]
\ No newline at end of file
+trimesh[easy]
+xs3d>=0.2.0
+zmesh>=1.4,<2.0
\ No newline at end of file
diff --git a/test/test_tasks.py b/test/test_tasks.py
index 27dce6a..908fee9 100755
--- a/test/test_tasks.py
+++ b/test/test_tasks.py
@@ -505,26 +505,40 @@ def test_contrast_normalization_task():
     tq.insert_all(tasks)
 
 
-def test_skeletonization_task():
+@pytest.mark.parametrize("cross_sectional_area", [False, True])
+def test_skeletonization_task(cross_sectional_area):
     directory = '/tmp/removeme/skeleton/'
     layer_path = 'file://' + directory
     delete_layer(layer_path)
 
-    img = np.ones((256,256,256), dtype=np.uint64)
+    img = np.ones((128,128,128), dtype=np.uint64)
     img[:,:,:] = 2
     cv = CloudVolume.from_numpy(
         img,
         layer_type='segmentation',
-        vol_path=layer_path, 
+        vol_path=layer_path,
     )
 
     tq = MockTaskQueue()
-    tasks = tc.create_skeletonizing_tasks(layer_path, mip=0, teasar_params={
-        'scale': 10,
-        'const': 10,
-    })
+    tasks = tc.create_skeletonizing_tasks(
+        layer_path,
+        mip=0,
+        teasar_params={
+            'scale': 10,
+            'const': 10,
+        },
+        cross_sectional_area=cross_sectional_area,
+        cross_sectional_area_smoothing_window=1,
+    )
     tq.insert_all(tasks)
 
+    cv = CloudVolume(layer_path)
+    skel = cv.skeleton.get(2)
+
+    assert len(skel.vertices) > 0
+    if cross_sectional_area:
+        assert len(skel.cross_sectional_area) > 0
+        assert np.all(skel.cross_sectional_area >= 0)
 
 def test_voxel_counting_task():
     directory = '/tmp/removeme/voxelcounting/'