diff --git a/pixi.lock b/pixi.lock
index 6900a66..d84af50 100644
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+  - jaraco-develop>=7.21 ; sys_platform != 'cygwin' and extra == 'type'
+  requires_python: '>=3.9'
 - pypi: https://files.pythonhosted.org/packages/13/02/58b0b8d9c17c93ab6340edd8b7308c0c5a5b81f94ce65705819b7416dba5/shapely-2.1.2-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
   name: shapely
   version: 2.1.2
@@ -3263,10 +3654,75 @@ packages:
   version: 1.17.0
   sha256: 4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274
   requires_python: '>=2.7,!=3.0.*,!=3.1.*,!=3.2.*'
+- pypi: https://files.pythonhosted.org/packages/e8/ae/fa6cd331b364ad2bbc31652d025f5747d89cbb75576733dfdf8efe3e4d62/slicerator-1.1.0-py3-none-any.whl
+  name: slicerator
+  version: 1.1.0
+  sha256: 167668d48c6d3a5ba0bd3d54b2688e81ee267dc20aef299e547d711e6f3c441a
 - pypi: https://files.pythonhosted.org/packages/32/46/9cb0e58b2deb7f82b84065f37f3bffeb12413f947f9388e4cac22c4621ce/sortedcontainers-2.4.0-py2.py3-none-any.whl
   name: sortedcontainers
   version: 2.4.0
   sha256: a163dcaede0f1c021485e957a39245190e74249897e2ae4b2aa38595db237ee0
+- pypi: https://files.pythonhosted.org/packages/b9/c1/16eb09d03eb7096b901e816dc93f53819f83b867c7d5dbb075f837d0120b/spatial_image-1.2.1-py3-none-any.whl
+  name: spatial-image
+  version: 1.2.1
+  sha256: 312c7f289284ed7f72eae50ca1b29b25b74db73f6daff745666af5ec27ad5436
+  requires_dist:
+  - numpy
+  - xarray-dataclasses
+  - xarray>=2024.10.0
+  requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/14/96/015832d04de478b5ccf089fd259bff58adcff195cf27fec29b2bed307a46/spatialdata-0.4.0-py3-none-any.whl
+  name: spatialdata
+  version: 0.4.0
+  sha256: f0a3f76efda57a221a775686a4d376093bfea560492697f40820eb3ec6d6ea89
+  requires_dist:
+  - anndata>=0.9.1
+  - click
+  - dask-image
+  - dask>=2024.4.1,<=2024.11.2
+  - fsspec
+  - geopandas>=0.14
+  - multiscale-spatial-image>=2.0.2
+  - networkx
+  - numba>=0.55.0
+  - numpy
+  - ome-zarr>=0.8.4
+  - pandas
+  - pooch
+  - pyarrow
+  - rich
+  - scikit-image
+  - scipy
+  - setuptools
+  - shapely>=2.0.1
+  - spatial-image>=1.1.0
+  - typing-extensions>=4.8.0
+  - xarray-dataclasses>=1.8.0
+  - xarray-schema
+  - xarray-spatial>=0.3.5
+  - xarray>=2024.10.0
+  - zarr<3
+  - asv ; extra == 'benchmark'
+  - bump2version ; extra == 'dev'
+  - ipython>=8.6.0 ; extra == 'docs'
+  - myst-nb ; extra == 'docs'
+  - sphinx-autobuild ; extra == 'docs'
+  - sphinx-autodoc-typehints ; extra == 'docs'
+  - sphinx-book-theme>=1.0.0 ; extra == 'docs'
+  - sphinx-copybutton ; extra == 'docs'
+  - sphinx-design ; extra == 'docs'
+  - sphinx-pytest ; extra == 'docs'
+  - sphinx>=4.5 ; extra == 'docs'
+  - sphinxcontrib-bibtex>=1.0.0 ; extra == 'docs'
+  - napari-spatialdata[all] ; extra == 'extra'
+  - spatialdata-io ; extra == 'extra'
+  - spatialdata-plot ; extra == 'extra'
+  - pytest ; extra == 'test'
+  - pytest-cov ; extra == 'test'
+  - pytest-mock ; extra == 'test'
+  - torch ; extra == 'test'
+  - torch ; extra == 'torch'
+  requires_python: '>=3.10,<3.13'
 - pypi: https://files.pythonhosted.org/packages/40/c6/9ae8e9b0721e9b6eb5f340c3a0ce8cd7cce4f66e03dd81f80d60f111987f/statsmodels-0.14.6-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
   name: statsmodels
   version: 0.14.6
@@ -3377,7 +3833,7 @@ packages:
   version: 1.1.0
   sha256: 15ccc861ac51c53696de0a5d6d4607f99c210739caf987b5d2054f3efed429d8
   requires_python: '>=3.9'
-- pypi: https://download.pytorch.org/whl/cu121/torch-2.5.1%2Bcu121-cp311-cp311-linux_x86_64.whl
+- pypi: https://download-r2.pytorch.org/whl/cu121/torch-2.5.1%2Bcu121-cp311-cp311-linux_x86_64.whl
   name: torch
   version: 2.5.1+cu121
   sha256: c8ab8c92eab928a93c483f83ca8c63f13dafc10fc93ad90ed2dcb7c82ea50410
@@ -3633,7 +4089,7 @@ packages:
   - kornia>=0.6.7 ; extra == 'dev'
   - statsmodels>0.13.5 ; extra == 'dev'
   requires_python: '>=3.10'
-- pypi: https://download.pytorch.org/whl/cu121/torchvision-0.20.1%2Bcu121-cp311-cp311-linux_x86_64.whl
+- pypi: https://download-r2.pytorch.org/whl/cu121/torchvision-0.20.1%2Bcu121-cp311-cp311-linux_x86_64.whl
   name: torchvision
   version: 0.20.1+cu121
   sha256: 237609a3551c2b68f32bcd3f3875dca93836010d83922ef2f3bd3a7540caee58
@@ -3680,7 +4136,7 @@ packages:
   - pytest ; extra == 'testing'
   - scikit-learn ; extra == 'testing'
   requires_python: '>=3.8'
-- pypi: https://download.pytorch.org/whl/triton-3.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
+- pypi: https://download-r2.pytorch.org/whl/triton-3.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
   name: triton
   version: 3.1.0
   sha256: 0f34f6e7885d1bf0eaaf7ba875a5f0ce6f3c13ba98f9503651c1e6dc6757ed5c
@@ -3698,10 +4154,6 @@ packages:
   - matplotlib ; extra == 'tutorials'
   - pandas ; extra == 'tutorials'
   - tabulate ; extra == 'tutorials'
-- pypi: https://download.pytorch.org/whl/typing_extensions-4.15.0-py3-none-any.whl
-  name: typing-extensions
-  version: 4.15.0
-  requires_python: '>=3.9'
 - pypi: https://files.pythonhosted.org/packages/18/67/36e9267722cc04a6b9f15c7f3441c2363321a3ea07da7ae0c0707beb2a9c/typing_extensions-4.15.0-py3-none-any.whl
   name: typing-extensions
   version: 4.15.0
@@ -3790,6 +4242,111 @@ packages:
   - pysocks>=1.5.6,!=1.5.7,<2.0 ; extra == 'socks'
   - backports-zstd>=1.0.0 ; python_full_version < '3.14' and extra == 'zstd'
   requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/71/71/4cd2151a236f44a6e2dd4ed8011838d7ba0be3d656c8bafdfc65a2ed1917/wrapt-2.3.0-cp311-cp311-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl
+  name: wrapt
+  version: 2.3.0
+  sha256: fc648a335d7e01adb3640b25f02fd0ea05886cf04d0af7f4ee902bc7b5e466e8
+  requires_dist:
+  - pytest ; extra == 'dev'
+  - setuptools ; extra == 'dev'
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/40/ed/1c4631ad5909487ea8907cd326d9855c2207d790e3936e77bda48173b8be/xarray-2024.11.0-py3-none-any.whl
+  name: xarray
+  version: 2024.11.0
+  sha256: 6ee94f63ddcbdd0cf3909d1177f78cdac756640279c0e32ae36819a89cdaba37
+  requires_dist:
+  - numpy>=1.24
+  - packaging>=23.2
+  - pandas>=2.1
+  - scipy ; extra == 'accel'
+  - bottleneck ; extra == 'accel'
+  - numbagg ; extra == 'accel'
+  - numba>=0.54 ; extra == 'accel'
+  - flox ; extra == 'accel'
+  - opt-einsum ; extra == 'accel'
+  - xarray[accel,etc,io,parallel,viz] ; extra == 'complete'
+  - hypothesis ; extra == 'dev'
+  - jinja2 ; extra == 'dev'
+  - mypy ; extra == 'dev'
+  - pre-commit ; extra == 'dev'
+  - pytest ; extra == 'dev'
+  - pytest-cov ; extra == 'dev'
+  - pytest-env ; extra == 'dev'
+  - pytest-xdist ; extra == 'dev'
+  - pytest-timeout ; extra == 'dev'
+  - ruff ; extra == 'dev'
+  - sphinx ; extra == 'dev'
+  - sphinx-autosummary-accessors ; extra == 'dev'
+  - xarray[complete] ; extra == 'dev'
+  - netcdf4 ; extra == 'io'
+  - h5netcdf ; extra == 'io'
+  - scipy ; extra == 'io'
+  - pydap ; python_full_version < '3.10' and extra == 'io'
+  - zarr ; extra == 'io'
+  - fsspec ; extra == 'io'
+  - cftime ; extra == 'io'
+  - pooch ; extra == 'io'
+  - sparse ; extra == 'etc'
+  - dask[complete] ; extra == 'parallel'
+  - cartopy ; extra == 'viz'
+  - matplotlib ; extra == 'viz'
+  - nc-time-axis ; extra == 'viz'
+  - seaborn ; extra == 'viz'
+  requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/aa/7b/9adbd82e2b7b8571da23856a890324b42cfbc38ae0ceac2177e5256f7216/xarray_dataclasses-1.9.1-py3-none-any.whl
+  name: xarray-dataclasses
+  version: 1.9.1
+  sha256: 3590556ef795ebbb33908b484a58311192d880cbb511666fe4c354569c3c468f
+  requires_dist:
+  - numpy>=1.22,<1.25 ; python_full_version == '3.8.*'
+  - numpy>=1.22,<3.0 ; python_full_version >= '3.9' and python_full_version < '3.14'
+  - typing-extensions>=4.0,<5.0
+  - xarray>=2022.3,<2023.2 ; python_full_version == '3.8.*'
+  - xarray>=2022.3,<2025.0 ; python_full_version >= '3.9' and python_full_version < '3.14'
+  requires_python: '>=3.8,<3.14'
+- pypi: https://files.pythonhosted.org/packages/a9/6d/f585a27b380ee987619b5617c0ca672a71a4345b67cfedbb6299750ce845/xarray_schema-0.0.3-py3-none-any.whl
+  name: xarray-schema
+  version: 0.0.3
+  sha256: aa6f856626b2e100213ba290407797464608b2555bb8e0b26093a97fe1ba38ce
+  requires_dist:
+  - xarray>=0.16
+  - numpy>=1.21
+  requires_python: '>=3.8'
+- pypi: https://files.pythonhosted.org/packages/61/ee/9d0bb9f1bbaf347efe2ca6ad7bae259d6ed9e7a2b4441bd16b88723145a1/xarray_spatial-0.5.3-py3-none-any.whl
+  name: xarray-spatial
+  version: 0.5.3
+  sha256: 84f5a3e07a1ac3212bcfff3c4677daa3c529a533f1493eb555f8e498b7d9746e
+  requires_dist:
+  - datashader>=0.15.0
+  - numba
+  - xarray
+  - numpy
+  - dask[dataframe] ; extra == 'doc'
+  - geopandas ; extra == 'doc'
+  - jinja2>=2.11 ; extra == 'doc'
+  - ipykernel ; extra == 'doc'
+  - matplotlib ; extra == 'doc'
+  - nbsphinx ; extra == 'doc'
+  - numpydoc ; extra == 'doc'
+  - pandoc ; extra == 'doc'
+  - pydata-sphinx-theme ; extra == 'doc'
+  - sphinx ; extra == 'doc'
+  - sphinx-panels ; extra == 'doc'
+  - sphinx-rtd-theme ; extra == 'doc'
+  - awkward>=1.4 ; extra == 'optional'
+  - geopandas ; extra == 'optional'
+  - shapely ; extra == 'optional'
+  - spatialpandas ; extra == 'optional'
+  - rtxpy ; extra == 'optional'
+  - flake8 ; extra == 'tests'
+  - isort ; extra == 'tests'
+  - noise>=1.2.2 ; extra == 'tests'
+  - dask ; extra == 'tests'
+  - pyarrow ; extra == 'tests'
+  - pytest ; extra == 'tests'
+  - pytest-cov ; extra == 'tests'
+  - scipy ; extra == 'tests'
+  requires_python: '>=3.8'
 - pypi: https://files.pythonhosted.org/packages/a5/86/cf2c0321dc3940a7aa73076f4fd677a0fb3e405cb297ead7d864fd90847e/xxhash-3.6.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl
   name: xxhash
   version: 3.6.0
@@ -3804,6 +4361,28 @@ packages:
   - multidict>=4.0
   - propcache>=0.2.1
   requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/5e/d8/9ffd8c237b3559945bb52103cf0eed64ea098f7b7f573f8d2962ef27b4b2/zarr-2.18.7-py3-none-any.whl
+  name: zarr
+  version: 2.18.7
+  sha256: ac3dc4033e9ae4e9d7b5e27c97ea3eaf1003cc0a07f010bd83d5134bf8c4b223
+  requires_dist:
+  - asciitree
+  - numpy>=1.24
+  - fasteners ; sys_platform != 'emscripten'
+  - numcodecs>=0.10.0,!=0.14.0,!=0.14.1,<0.16
+  - notebook ; extra == 'jupyter'
+  - ipytree>=0.2.2 ; extra == 'jupyter'
+  - ipywidgets>=8.0.0 ; extra == 'jupyter'
+  - sphinx ; extra == 'docs'
+  - sphinx-automodapi ; extra == 'docs'
+  - sphinx-design ; extra == 'docs'
+  - sphinx-issues ; extra == 'docs'
+  - sphinx-copybutton ; extra == 'docs'
+  - pydata-sphinx-theme ; extra == 'docs'
+  - numpydoc ; extra == 'docs'
+  - numcodecs[msgpack]!=0.14.0,!=0.14.1,<0.16 ; extra == 'docs'
+  - pytest-doctestplus ; extra == 'docs'
+  requires_python: '>=3.11'
 - pypi: https://files.pythonhosted.org/packages/44/15/bb13b4913ef95ad5448490821eee4671d0e67673342e4d4070854e5fe081/zarr-3.1.5-py3-none-any.whl
   name: zarr
   version: 3.1.5
diff --git a/pixi.toml b/pixi.toml
index b27ccad..7d048d2 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -37,9 +37,11 @@ cudf-cu12 = "==24.10.*"
 cuml-cu12 = "==24.10.*"
 cugraph-cu12 = "==24.10.*"
 cupy-cuda12x = ">=12.2,<13.0"
+spatialdata = ">=0.4.0, <0.5"
+setuptools = ">=81.0.0, <82"
 
 [tool.uv.extra-build-dependencies]
 torch-scatter = ["torch"]
 
 [environments]
-cuda121 = ["cuda121"]
\ No newline at end of file
+cuda121 = ["cuda121"]
diff --git a/src/segger/__init__.py b/src/segger/__init__.py
index 67cb121..a4ef99d 100644
--- a/src/segger/__init__.py
+++ b/src/segger/__init__.py
@@ -1,6 +1,11 @@
 import logging
 import os
 from pathlib import Path
+
+import dask
+
+dask.config.set({"dataframe.query-planning": False})  # spatialdata doesn't yet support dask-expr; must be set before cudf pulls in dask.dataframe
+
 import cupy as cp
 import torch
 import rmm
diff --git a/src/segger/cli/export.py b/src/segger/cli/export.py
index 7d0b6a8..8bbdfdf 100644
--- a/src/segger/cli/export.py
+++ b/src/segger/cli/export.py
@@ -1,104 +1,270 @@
 """``segger export``: write a segger segmentation as scverse-compatible files.
 
-One command writes the chosen SpatialData elements: ``anndata`` the cell by gene table
-(``adata.h5ad``), ``transcripts`` the assigned transcripts/points (``transcripts.parquet``),
-and ``boundaries`` one polygon per cell/shapes (``cell_boundaries.parquet``). Default: anndata + boundaries.
+Example usage:
+    # save anndata and boundaries
+    segger export anndata \
+      -s $PATH_OUTPUT/segger_segmentation.parquet \
+      -o $PATH_OUTPUT/adata_export
+
+    # save spatialdata - adds elements directly to an existing sdata object, no -o needed
+    segger export spatialdata \
+      -s $PATH_OUTPUT/segger_segmentation.parquet \
+      --sdata $PATH_INPUT/sdata.zarr
 """
 
 from __future__ import annotations
 
 from pathlib import Path
 from typing import Annotated, Literal, Optional
-
 from cyclopts import Parameter, Group, validators
 
+import polars as pl
+from ..io import StandardTranscriptFields
+
 _group_io = Group(name="I/O", sort_key=0)
 _group_opts = Group(name="Options", sort_key=1)
 
-_Element = Literal["anndata", "transcripts", "boundaries"]
+_Element = Literal["anndata", "transcripts", "boundaries", "spatialdata"]
 _DEFAULT_ELEMENTS = ("anndata", "boundaries")
 
 _Seg = Annotated[Path, Parameter(alias="-s", group=_group_io, validator=validators.Path(exists=True, dir_okay=False))]
-_Source = Annotated[Path, Parameter(alias="-i", group=_group_io, validator=validators.Path(exists=True, dir_okay=True))]
-_Out = Annotated[Path, Parameter(alias="-o", group=_group_io)]
+_Source = Annotated[
+    Optional[Path],
+    Parameter(
+        alias="-i",
+        group=_group_io,
+        validator=validators.Path(exists=True, dir_okay=True),
+        help="Source transcripts directory. Only needed for segger v0.2.0 (before x/y/feature_name were included in outputs).",
+    ),
+]
+_Out = Annotated[
+    Optional[Path],
+    Parameter(alias="-o", group=_group_io, help="Required unless the only element being exported is 'spatialdata'."),
+]
+_Sdata = Annotated[
+    Optional[Path],
+    Parameter(
+        alias="--sdata",
+        group=_group_io,
+        validator=validators.Path(exists=True, dir_okay=True),
+        help="Existing SpatialData Zarr store to add elements to in place (required for 'spatialdata').",
+    ),
+]
 
 _IncludeAll = Annotated[
     bool,
-    Parameter(group=_group_opts, help="Keep every cell-assigned transcript, ignoring the similarity threshold."),
+    Parameter(group=_group_opts, help="Keep every transcript in the segmentation output, not just the ones segger's 'filtered' flag marked as kept."),
+]
+_SdataTranscriptsName = Annotated[
+    str,
+    Parameter(
+        group=_group_opts,
+        help="Name of the existing points element in --sdata to append segger's columns to.",
+    ),
 ]
-_MinSim = Annotated[
-    Optional[float],
+_SdataCellBoundariesName = Annotated[
+    str,
     Parameter(
         group=_group_opts,
-        validator=validators.Number(gte=0, lte=1),
-        help="Fixed similarity threshold (0-1), overriding the per-gene threshold from segmentation.",
+        help="Name of the shapes element segger's cell boundaries are written to "
+        "(avoids colliding with an existing same-named element, e.g. from the raw Xenium sdata).",
     ),
 ]
-_MinTx = Annotated[
+_SdataTableName = Annotated[
+    str,
+    Parameter(
+        group=_group_opts,
+        help="Name of the table element segger's AnnData is written to.",
+    ),
+]
+_MinCounts = Annotated[
     int,
     Parameter(
         group=_group_opts,
-        validator=validators.Number(gte=0),
-        help="Minimum number of assigned transcripts a cell must have to be included.",
+        help="Drop cells with fewer than this many assigned transcripts from the table/anndata "
+        "(must be >= 3 for spatialdata, since boundaries need >= 3 points).",
     ),
 ]
 
 
-def _load_assigned(
+# -- LOAD TRANSCRIPTS --
+def load_transcripts(
     segmentation_path: Path,
-    source_path: Path,
     include_all_transcripts: bool,
-    min_similarity: Optional[float],
-    min_transcripts: int = 10,
-) -> "pl.DataFrame":
-    """Join predictions onto source transcripts; return the kept tx (row_index/segger_cell_id/feature_name/x/y)."""
-    import polars as pl
-
-    from ..io import StandardTranscriptFields, get_preprocessor
+    source_path: Path = None,
+):
+    # read transcripts
+    tx = pl.read_parquet(segmentation_path)
 
+    # check if legacy result (before v0.2.0)
     std = StandardTranscriptFields()
-    seg = pl.read_parquet(segmentation_path)
-    if "segger_cell_id" not in seg.columns:
-        raise ValueError(f"No 'segger_cell_id' column in {segmentation_path}.")
-
-    tx = get_preprocessor(source_path).transcripts
-    if isinstance(tx, pl.LazyFrame):
-        tx = tx.collect()
-
-    pred_cols = [c for c in (std.row_index, "segger_cell_id", "segger_similarity", "similarity_threshold") if c in seg.columns]
-    merged = tx.join(seg.select(pred_cols), on=std.row_index, how="left")
-
-    has_assignment = pl.col("segger_cell_id").is_not_null()
-    if include_all_transcripts:
-        keep = has_assignment
-    elif min_similarity is not None:
-        if "segger_similarity" not in merged.columns:
-            raise ValueError("--min-similarity needs a 'segger_similarity' column in the segmentation file.")
-        keep = has_assignment & (pl.col("segger_similarity") >= min_similarity)
-    elif {"segger_similarity", "similarity_threshold"} <= set(merged.columns):
-        keep = has_assignment & (pl.col("segger_similarity") >= pl.col("similarity_threshold"))
-    else:
-        keep = has_assignment
+    if not {std.x, std.y, std.feature, "filtered"} <= set(tx.columns):
+        tx = _legacy_join(tx, source_path=source_path, std=std)
 
-    assigned = merged.filter(keep).select(
+    # subset
+    coord_cols = [pl.col(std.x).alias("x"), pl.col(std.y).alias("y")]
+    if std.z in tx.columns:
+        coord_cols.append(pl.col(std.z).alias("z"))
+
+    # filter
+    kept = tx.filter(pl.col("filtered")) if not include_all_transcripts else tx
+
+    # select
+    assigned = kept.select(
         pl.col(std.row_index),
         pl.col("segger_cell_id").cast(pl.String),
         pl.col(std.feature).alias("feature_name"),
-        pl.col(std.x).alias("x"),
-        pl.col(std.y).alias("y"),
+        *coord_cols,
+    )
+
+    return assigned, tx
+
+
+def _legacy_join(tx: "pl.DataFrame", source_path: Optional[Path], std) -> "pl.DataFrame":
+    """Join x/y/feature_name onto a segmentation output written before they were included inline."""
+    if source_path is None:
+        raise ValueError("This segmentation output predates inline x/y/feature_name; pass -i/--source-path to join them from the source transcripts.")
+
+    from ..io import get_preprocessor
+    
+    # load and merge
+    tx_all = get_preprocessor(source_path).transcripts
+    pred_cols = [c for c in (std.row_index, "segger_cell_id", "segger_similarity", "similarity_threshold") if c in tx.columns]
+    tx = tx.select(pred_cols).join(tx_all, on=std.row_index, how="left")
+
+    # add "filtered"
+    tx = tx.with_columns((
+        (pl.col("segger_cell_id").is_not_null()) & (pl.col("segger_similarity") >= pl.col("similarity_threshold"))
+    ).alias("filtered"))
+
+    # if "converged" exists, also require this to be true
+    if "converged" in tx.columns:
+        tx = tx.with_columns(
+            (pl.col("filtered") & pl.col("converged")).alias("filtered")
+        )
+    
+    return tx
+
+# -- Spatial Data Support
+def _check_sdata_elements(
+        sdata,
+        transcripts_element: str,
+        cell_boundaries_element: str,
+        table_element: str,
+    ) -> None:
+    """Fail fast if the transcripts element is missing, or any target element name already exists."""
+    if transcripts_element not in sdata.points:
+        raise KeyError(f"{transcripts_element!r} not found in sdata.points; pass --sdata-transcripts-name to point at the right one.")
+    if cell_boundaries_element in sdata.shapes:
+        raise FileExistsError(f"{cell_boundaries_element!r} already exists in sdata.shapes; pass --sdata-cell-boundaries-name to specify a different name.")
+    if table_element in sdata.tables:
+        raise FileExistsError(f"{table_element!r} already exists in sdata.tables; pass --sdata-table-name to specify a different name.")
+
+
+
+def _merge_sdata_transcripts(sdata_tx: "dd.DataFrame", tx: "pl.DataFrame", row_index: str) -> "dd.DataFrame":
+    """Segger's per-transcript outputs, joined onto the existing (dask-backed) transcripts table. Stays lazy throughout."""
+
+    # rename; these names are hardcoded. make sure to update this if any name should change going forward
+    map_columns = {
+        "segger_cell_id": "segger_cell_id",
+        "segger_similarity": "segger_similarity",
+        "similarity_threshold": "segger_similarity_threshold",
+        "converged": "segger_converged",
+        "filtered": "segger_filtered",
+    }
+
+    # rename
+    tx = tx.rename(map_columns).select(row_index, *map_columns.values()).to_pandas()
+
+    # sdata_tx has no row_index column; construct it
+    sdata_tx = sdata_tx.assign(**{row_index: 1})
+    sdata_tx[row_index] = sdata_tx[row_index].cumsum() - 1
+
+    # delete existing (overwrite is not exactly silent - export would stop if boundaries or table already exists)
+    sdata_tx = sdata_tx.drop(columns=[col for col in map_columns.values() if col in sdata_tx.columns])
+
+    # merge (tx is small enough to broadcast onto every partition; no shuffle needed)
+    sdata_tx = sdata_tx.merge(tx, on=row_index, how="left")
+    sdata_tx["segger_seen"] = sdata_tx["segger_filtered"].notnull()
+
+    # row_index is only a join key; drop it from the output
+    sdata_tx = sdata_tx.drop(columns=[row_index])
+
+    return sdata_tx
+
+def _write_to_sdata(
+    sdata,
+    tx: "pl.DataFrame",
+    gdf: "gpd.GeoDataFrame",
+    adata: "AnnData",
+    transcripts_element: str = "transcripts",
+    cell_boundaries_element: str = "cell_boundaries_segger",
+    table_element: str = "table_segger",
+) -> None:
+    """Append segger's columns to the existing transcripts element, and add its boundaries/table elements to the given SpatialData store."""
+    from spatialdata.models import PointsModel, ShapesModel, TableModel
+    from spatialdata.transformations import get_transformation
+
+    # get current transcripts
+    base_transcripts = sdata.points[transcripts_element]
+    attrs = base_transcripts.attrs.get("spatialdata_attrs", {})
+    transformations = get_transformation(base_transcripts, get_all=True)
+
+    # new element fully in memory, required for overwriting
+    tx = _merge_sdata_transcripts(base_transcripts, tx, "row_index").compute().reset_index(drop=True)
+
+    coordinates = {"x": "x", "y": "y"}
+    if "z" in tx.columns:
+        coordinates["z"] = "z"
+
+    # build the new (in-memory, unbacked) elements
+    new_transcripts = PointsModel.parse(
+        tx,
+        coordinates=coordinates,
+        feature_key=attrs.get("feature_key"),
+        instance_key=attrs.get("instance_key"),
+        transformations=transformations,
     )
+    new_boundaries = ShapesModel.parse(gdf, transformations=transformations)
+    new_table = TableModel.parse(adata)
 
-    if min_transcripts > 0:
-        assigned = assigned.filter(pl.len().over("segger_cell_id") >= min_transcripts)
+    # SpatialData can't overwrite (#520). Do 1) backup, 2) drop in-memory handles, 3) delete original, 4) write new elements, 5) drop backup
+    # 1 backup
+    backup_element = f"{transcripts_element}_backup"
+    sdata[backup_element] = base_transcripts
+    sdata.write_element([backup_element])
 
-    return assigned
+    # 2 detach in-memory
+    del sdata.points[backup_element]
+    sdata[transcripts_element] = new_transcripts
+    sdata[cell_boundaries_element] = new_boundaries
+    sdata[table_element] = new_table
+
+    # 3 4 delete and write
+    print(f"Writing {transcripts_element}, {cell_boundaries_element}, {table_element} to {sdata.path}...")
+    try:
+        sdata.delete_element_from_disk(transcripts_element)
+        sdata.write_element([transcripts_element, cell_boundaries_element, table_element])
+    except Exception:
+        print(
+            f"Write failed. The original {transcripts_element!r} is preserved on disk as "
+            f"{backup_element!r}; restore it from there."
+        )
+        raise
+    else:
+        # delete backup
+        sdata.delete_element_from_disk(backup_element)
+        if sdata.has_consolidated_metadata():
+            sdata.write_consolidated_metadata()
 
 
 def export(
     *elements: Annotated[_Element, Parameter(help="Elements to write (default: anndata boundaries).")],
     segmentation_path: _Seg,
-    source_path: _Source,
-    output_directory: _Out,
+    output_directory: _Out = None,
+    source_path: _Source = None,
+    sdata_path: _Sdata = None,
     method: Annotated[
         Literal["delaunay", "convex_hull"],
         Parameter(group=_group_opts, help="Cell-polygon method for boundaries."),
@@ -106,31 +272,75 @@ def export(
     chaikin_iterations: Annotated[
         int, Parameter(group=_group_opts, help="Chaikin corner-cutting iterations to round boundaries (0 disables).")
     ] = 0,
-    include_all_transcripts: _IncludeAll = False,
-    min_similarity: _MinSim = None,
-    min_transcripts: _MinTx = 10,
+    include_all_transcripts: _IncludeAll = True,
+    sdata_transcripts_name: _SdataTranscriptsName = "transcripts",
+    sdata_cell_boundaries_name: _SdataCellBoundariesName = "cell_boundaries_segger",
+    sdata_table_name: _SdataTableName = "table_segger",
+    min_counts: _MinCounts = 10,
 ):
-    """Write a segger segmentation as scverse SpatialData elements (anndata, transcripts, boundaries)."""
+    """Write a segger segmentation as scverse SpatialData elements (anndata, transcripts, boundaries, spatialdata)."""
     selected = elements or _DEFAULT_ELEMENTS
-    assigned = _load_assigned(segmentation_path, source_path, include_all_transcripts, min_similarity, min_transcripts)
-    output_directory.mkdir(parents=True, exist_ok=True)
 
+    sdata = None
+    if "spatialdata" in selected:
+        if min_counts < 3:
+            # cell boundaries need >= 3 transcripts to form a polygon; a lower table cutoff leaves cells without one
+            raise ValueError("--min-counts must be >= 3 for spatialdata: boundaries need >= 3 transcripts to form a polygon.")
+        if sdata_path is None:
+            raise ValueError("--sdata is required when exporting 'spatialdata'.")
+        import spatialdata as sd
+        sdata = sd.read_zarr(sdata_path)
+        _check_sdata_elements(sdata, sdata_transcripts_name, sdata_cell_boundaries_name, sdata_table_name)
+
+    if set(selected) - {"spatialdata"} and output_directory is None:
+        raise ValueError("-o/--output-directory is required unless the only element being exported is 'spatialdata'.")
+
+    # load tx
+    assigned, tx = load_transcripts(segmentation_path, include_all_transcripts, source_path)
+    if output_directory is not None:
+        output_directory.mkdir(parents=True, exist_ok=True)
+
+    # compute outputs
     gdf = None
-    if "boundaries" in selected:
+    if "boundaries" in selected or "spatialdata" in selected:
         from ..export import generate_boundaries
-
         gdf = generate_boundaries(assigned, cell_id="segger_cell_id", method=method, smoothing=chaikin_iterations)
+
+    adata = None
+    if "anndata" in selected or "spatialdata" in selected:
+        from ..export import build_anndata
+        adata = build_anndata(
+            assigned,
+            cell_id="segger_cell_id",
+            z="z",
+            area=gdf.geometry.area if gdf is not None else None,
+            region=sdata_cell_boundaries_name,
+            min_counts=min_counts,
+        )
+        # keep only cells that produced a boundary polygon, so the table annotates only real shapes
+        if gdf is not None:
+            adata = adata[adata.obs_names.isin(gdf.index)].copy()
+
+    # save outputs
+    if "transcripts" in selected:
+        assigned.write_parquet(output_directory / "transcripts.parquet")
+        print(f"Wrote {assigned.height} assigned transcripts: {output_directory / 'transcripts.parquet'}")
+
+    if "boundaries" in selected:
         gdf.to_parquet(output_directory / "cell_boundaries.parquet")
         print(f"Wrote {len(gdf)} {method} cell boundaries: {output_directory / 'cell_boundaries.parquet'}")
 
     if "anndata" in selected:
-        from ..export import build_anndata
-
-        # Use the exported polygon areas so obs["area"] matches the boundaries; omitted otherwise.
-        adata = build_anndata(assigned, cell_id="segger_cell_id", area=gdf.geometry.area if gdf is not None else None)
         adata.write_h5ad(output_directory / "adata.h5ad")
         print(f"Wrote AnnData ({adata.n_obs} cells x {adata.n_vars} genes): {output_directory / 'adata.h5ad'}")
 
-    if "transcripts" in selected:
-        assigned.write_parquet(output_directory / "transcripts.parquet")
-        print(f"Wrote {assigned.height} assigned transcripts: {output_directory / 'transcripts.parquet'}")
+    if "spatialdata" in selected:
+        _write_to_sdata(
+            sdata,
+            tx,
+            gdf,
+            adata,
+            transcripts_element=sdata_transcripts_name,
+            cell_boundaries_element=sdata_cell_boundaries_name,
+            table_element=sdata_table_name,
+        )
diff --git a/src/segger/data/writer.py b/src/segger/data/writer.py
index dfc1139..7f62be5 100644
--- a/src/segger/data/writer.py
+++ b/src/segger/data/writer.py
@@ -110,6 +110,10 @@ def write_anndata(
         tx_fields = TrainingTranscriptFields()
 
         tx = trainer.datamodule.tx
+        coordinate_columns = [tx_fields.x, tx_fields.y]
+        if tx_fields.z in tx.columns:
+            coordinate_columns.append(tx_fields.z)
+
         transcripts = (
             segmentation
             .filter(
@@ -118,8 +122,7 @@ def write_anndata(
             .join(
                 tx.select([
                     tx_fields.row_index,
-                    tx_fields.x,
-                    tx_fields.y,
+                    *coordinate_columns,
                     tx_fields.feature,
                 ]),
                 on=tx_fields.row_index,
@@ -132,8 +135,7 @@ def write_anndata(
                 "segger_cell_id",
                 "segger_similarity",
                 "similarity_threshold",
-                tx_fields.x,
-                tx_fields.y,
+                *coordinate_columns,
             ])
         )
 
@@ -142,7 +144,7 @@ def write_anndata(
             feature_column="segger_gene",
             cell_id_column="segger_cell_id",
             score_column="segger_similarity",
-            coordinate_columns=[tx_fields.x, tx_fields.y],
+            coordinate_columns=coordinate_columns,
         )
         adata.write_h5ad(self.output_directory / 'segger_anndata.h5ad')
 
diff --git a/src/segger/export/anndata_writer.py b/src/segger/export/anndata_writer.py
index b957e56..e9d3989 100644
--- a/src/segger/export/anndata_writer.py
+++ b/src/segger/export/anndata_writer.py
@@ -15,19 +15,25 @@ def build_anndata(
     feature: str = "feature_name",
     x: str = "x",
     y: str = "y",
+    z: Optional[str] = None,
     region: str = "cell_boundaries",
     area: Optional[pd.Series] = None,
+    min_counts: int = 1,
 ) -> AnnData:
     """Cell x gene table built on :func:`anndata_from_transcripts`, with the SpatialData link added.
 
-    ``obs`` is indexed by the cell id; centroids land in ``obsm["spatial"]`` and the table-to-shapes
-    link in ``uns["spatialdata_attrs"]``. ``area`` (a per-cell Series, e.g. the exported boundary
-    polygon areas) is written to ``obs["area"]`` when given.
+    ``obs`` is indexed by cell id, with centroids in ``obsm["spatial"]`` (3D if ``z`` is given and
+    present) and the table-to-shapes link in ``uns["spatialdata_attrs"]``. ``area``, when given, is
+    written to ``obs["area"]``.
     """
     from ..data.utils.anndata import anndata_from_transcripts
 
+    coordinate_columns = [x, y]
+    if z is not None and z in assigned.columns:
+        coordinate_columns.append(z)
+
     adata = anndata_from_transcripts(
-        assigned, feature_column=feature, cell_id_column=cell_id, coordinate_columns=[x, y]
+        assigned, feature_column=feature, cell_id_column=cell_id, coordinate_columns=coordinate_columns
     )
     if "X_spatial" in adata.obsm:
         adata.obsm["spatial"] = adata.obsm.pop("X_spatial")
@@ -37,6 +43,9 @@ def build_anndata(
     if area is not None:
         adata.obs["area"] = pd.Series(area).reindex(adata.obs_names).to_numpy()
 
+    if min_counts > 1:
+        adata = adata[adata.obs["n_transcripts"] >= min_counts].copy()
+
     # SpatialData link: region/instance_key obs columns plus the attrs that join table to shapes.
     adata.obs["region"] = pd.Categorical([region] * adata.n_obs, categories=[region])
     adata.obs["cell_id"] = adata.obs_names.to_numpy()
diff --git a/src/segger/export/boundary.py b/src/segger/export/boundary.py
index 65ea72b..3307d90 100644
--- a/src/segger/export/boundary.py
+++ b/src/segger/export/boundary.py
@@ -7,6 +7,8 @@
 
 from __future__ import annotations
 
+import os
+from concurrent.futures import ProcessPoolExecutor
 from typing import Literal, Optional, Union
 
 import geopandas as gpd
@@ -184,6 +186,13 @@ def cell_boundary(
     return poly
 
 
+def _build_one_boundary(inputs: tuple) -> tuple:
+    """Run ``cell_boundary`` for one cell; returns (cell_id, n_transcripts, geometry)."""
+    cid, pts, method, smoothing, connectivity = inputs
+    geom = cell_boundary(pts, method=method, smoothing=smoothing, connectivity=connectivity)
+    return str(cid), len(pts), geom
+
+
 def generate_boundaries(
     transcripts: Union[pl.DataFrame, pd.DataFrame],
     cell_id: str = "cell_id",
@@ -203,14 +212,20 @@ def generate_boundaries(
         n_groups = grouped.ngroups
         groups = ((cid, g[[x, y]].to_numpy()) for cid, g in grouped)
 
-    ids, n_tx, geoms = [], [], []
-    for cid, pts in tqdm(groups, total=n_groups, desc="Building cell boundaries"):
-        ids.append(str(cid))
-        n_tx.append(len(pts))
-        geoms.append(cell_boundary(pts, method=method, smoothing=smoothing, connectivity=connectivity))
+    inputs = [(cid, pts, method, smoothing, connectivity) for cid, pts in groups]
+
+    n_workers = max(len(os.sched_getaffinity(0)) - 1, 1)
+    with ProcessPoolExecutor(max_workers=n_workers) as pool:
+        results = list(
+            tqdm(
+                pool.map(_build_one_boundary, inputs, chunksize=10),
+                total=n_groups,
+                desc="Building cell boundaries",
+            )
+        )
+    ids, n_tx, geoms = map(list, zip(*results)) if results else ([], [], [])
 
-    # Output the SpatialData instance key as "cell_id" regardless of the input column name. Keep it as
-    # a column too: geoparquet drops a named index, and it must match the table instance key to join.
+    # Output the SpatialData instance key as "cell_id" regardless of the input column name.
     gdf = gpd.GeoDataFrame(
         {"cell_id": ids, "n_transcripts": n_tx}, geometry=geoms, index=pd.Index(ids, name="cell_id")
     )
diff --git a/src/segger/io/fields.py b/src/segger/io/fields.py
index 8dc30a9..315965c 100644
--- a/src/segger/io/fields.py
+++ b/src/segger/io/fields.py
@@ -107,6 +107,7 @@ class StandardTranscriptFields:
     row_index: str = 'row_index'
     x: str = 'x'
     y: str = 'y'
+    z: str = 'z'
     feature: str = 'feature_name'
     cell_id: str = 'cell_id'
     compartment: str = 'cell_compartment'