new release v2.5.3

.github/dependabot.yml

@@ -0,0 +1,6 @@
+version: 2
+updates:
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "weekly"

.github/workflows/code-style.yml

@@ -14,11 +14,11 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           path: 'src'
       - name: Install uv
-        uses: astral-sh/setup-uv@v5
+        uses: astral-sh/setup-uv@v7
         with:
           python-version: '3.10'
       - name: Run Ruff

.github/workflows/deploy.yml

@@ -20,11 +20,11 @@ jobs:
       - name: Get repository name.
         run: echo "FASTSURFER_DIR=$GITHUB_WORKSPACE" >> $GITHUB_ENV
       - name: Checkout repository
-        uses: actions/checkout@v6.0.2
+        uses: actions/checkout@v6
         with:
           fetch-depth: 0
       - name: Set up python environment
-        uses: actions/setup-python@v6.2.0
+        uses: actions/setup-python@v6
         with:
           python-version: '3.10'
       - name: install dependencies

.github/workflows/doc.yml

@@ -19,11 +19,11 @@ jobs:
         shell: bash
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           path: src
       - name: Install uv
-        uses: astral-sh/setup-uv@v5
+        uses: astral-sh/setup-uv@v7
         with:
           python-version: '3.12'
       - name: Build doc
@@ -33,7 +33,7 @@ jobs:
         run: |
           uv run --project src --extra doc sphinx-build -WT --keep-going -j auto src/doc doc-build
       - name: Upload documentation
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v7
         with:
           name: doc
           path: |

.github/workflows/quicktest.yaml

@@ -132,15 +132,15 @@ jobs:
       - name: Checkout repository
         # DOCKER_IMAGE is build-cached => we want to build, check this repository out to build the docker image
         if: steps.parse.outputs.CONTINUE == 'true' && steps.parse.outputs.DOCKER_IMAGE == 'build-cached'
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
       - name: Checkout repository
         # DOCKER_IMAGE starts with pr/*** => we want to build, check the PR out to build the docker image
         if: steps.parse.outputs.CONTINUE == 'true' && startsWith(steps.parse.outputs.DOCKER_IMAGE, 'pr/')
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
         with:
           ref: ${{ steps.parse.outputs.DOCKER_IMAGE }}
       - name: Setup uv and Python 3.11
-        uses: astral-sh/setup-uv@v5
+        uses: astral-sh/setup-uv@v7
         with:
           python-version: '3.11'
           # python 3.11 is needed to read pyproject.toml (tomllib)
@@ -195,7 +195,7 @@ jobs:
             extra-args: "--3T"
     steps:
       - name: Check out the repository that is to be tested
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
       - name: Run FastSurfer for ${{ matrix.subject-id }} with the previously created docker container
         uses: Deep-MI/FastSurfer/.github/actions/run-fastsurfer@dev
         with:
@@ -221,7 +221,7 @@ jobs:
     if: always() && needs.build-docker.outputs.continue == 'true'
     steps:
       - name: Retrieve test JUnit files
-        uses: actions/download-artifact@v4
+        uses: actions/download-artifact@v8
         with:
           pattern: fastsurfer-${{ github.sha }}-junit-*
           merge-multiple: 'true'

.github/workflows/unittest.yaml

@@ -39,9 +39,9 @@ jobs:
         pytest-flags: [""]
 
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
       - name: Setup Python 3.10
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: '3.10'
           architecture: 'x64'
@@ -71,7 +71,7 @@ jobs:
           python -m pytest "${flags[@]}" ${{ matrix.pytest-flags }} test/${{ matrix.tests }}
           echo "::endgroup::"
       - name: Upload the JUnit XML file as an artifact
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v7
         with:
           name: fastsurfer-${{ github.sha }}-${{ matrix.tests }}-junit
           path: /tmp/fastsurfer-unittest-${{ matrix.tests }}.junit.xml

CorpusCallosum/shape/contour.py

@@ -320,6 +320,11 @@ def fill_thickness_values(self) -> None:
             closest_indices = known_idx[np.argsort(distances)[:2]]
             closest_distances = np.sort(distances)[:2]
 
+            zero_distance = closest_distances <= 1e-10
+            if np.any(zero_distance):
+                thickness[j] = np.mean(thickness[closest_indices[zero_distance]])
+                continue
+
             # Calculate weights based on inverse distance
             weights = 1.0 / closest_distances
             weights = weights / np.sum(weights)

CorpusCallosum/shape/thickness.py

@@ -113,7 +113,7 @@ def insert_point_with_thickness(
     point: np.ndarray,
     thickness_value: float,
     return_index: Literal[True],
-) -> tuple[np.ndarray, np.ndarray, int] | list[np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray, int]:
     ...
 
 
@@ -148,6 +148,49 @@ def insert_point_with_thickness(
     insertion_index : int
         The index, where the point was inserted (only if return_index is True).
     """
+    existing_distances = np.linalg.norm(contour_in_as_space[:, :2] - point[:2], axis=1)
+    existing_matches = np.where(existing_distances <= 1e-10)[0]
+    if len(existing_matches) > 0:
+        point_idx = int(existing_matches[0])
+        if np.isnan(contour_thickness[point_idx]):
+            contour_thickness[point_idx] = thickness_value
+
+        # Keep the contour cardinality stable across slices, but avoid inserting
+        # an exact duplicate vertex. Exact duplicates create zero-length edges in
+        # visualization/mesh code, while skipping the point breaks the equal
+        # point-count invariant expected by CCMesh.from_contours().
+        before_idx = (point_idx - 1) % len(contour_in_as_space)
+        after_idx = (point_idx + 1) % len(contour_in_as_space)
+        before_vector = contour_in_as_space[before_idx, :2] - contour_in_as_space[point_idx, :2]
+        after_vector = contour_in_as_space[after_idx, :2] - contour_in_as_space[point_idx, :2]
+        before_length = np.linalg.norm(before_vector)
+        after_length = np.linalg.norm(after_vector)
+
+        if after_length >= before_length and after_length > 0:
+            insert_idx = point_idx + 1
+            direction = after_vector / after_length
+            edge_length = after_length
+        elif before_length > 0:
+            insert_idx = point_idx
+            direction = before_vector / before_length
+            edge_length = before_length
+        else:
+            insert_idx = point_idx + 1
+            direction = np.zeros_like(point[:2])
+            edge_length = 0.0
+
+        if edge_length > 0:
+            step = min(max(edge_length * 1e-6, 1e-8), edge_length * 0.5)
+            point = contour_in_as_space[point_idx, :2] + direction * step
+
+        contour_in_as_space = np.insert(contour_in_as_space, insert_idx, point, axis=0)
+        contour_thickness = np.insert(contour_thickness, insert_idx, thickness_value)
+
+        if return_index:
+            return contour_in_as_space, contour_thickness, insert_idx
+        else:
+            return contour_in_as_space, contour_thickness
+
     # Find closest edge for the point
     edge_idx = find_closest_edge(point, contour_in_as_space)
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = 'setuptools.build_meta'
 
 [project]
 name = 'fastsurfer'
-version = '2.5.2'
+version = '2.5.3'
 description = 'A fast and accurate deep-learning based neuroimaging pipeline'
 readme = 'README.md'
 license = {file = 'LICENSE'}
@@ -38,7 +38,7 @@ dependencies = [
     'matplotlib>=3.7.1',
     'meshpy>=2025.1.1', # needed for FastSurfer-CC
     'monai>=1.4.0', # needed for FastSurfer-CC
-    'neuroreg>=0.6.1', # needed for registration / etiv
+    'neuroreg>=0.6.2', # needed for registration / etiv
     'nibabel>=5.4.0', # needed to fix a bug in nibabel
     'numpy>=1.25',
     'packaging',

recon_surf/recon-surf.sh

@@ -714,7 +714,7 @@ for hemi in lh rh ; do
 
       # Check if the surfaceRAS was correctly set and exit otherwise (sanity check in case nibabel changes their default header behaviour)
       {
-        cmd="mris_info $outmesh | tr -s ' ' | grep -q 'vertex locs : surfaceRAS'"
+        cmd="mris_info $outmesh | awk '\$1 == \"vertex\" && \$2 == \"locs\" && \$3 == \":\" && \$4 == \"surfaceRAS\" { found = 1 } END { exit !found }'"
         echo "echo \"$cmd\""
         echo "$timecmd $cmd"
       } | tee -a "$CMDF"

recon_surf/smooth_aparc.py

@@ -177,7 +177,7 @@ def mode_filter(
     # for num rings exponentiate adjM and add adjM from step before
     # we currently do this outside of mode_filter
     # new labels will be the same as old almost everywhere
-    labels_new = labels
+    labels_new = labels.copy()
     # find vertices to fill
     # if fillonlylabels empty, fill all
     if not fillonlylabel:
@@ -204,8 +204,6 @@ def mode_filter(
     # create sparse matrix with labels at neighbors
     nlabels = sparse.csr_matrix((labels[JJ], (II, JJ)))
     # print("nlabels: {}".format(nlabels))
-    from scipy.stats import mode
-
     if not isinstance(nlabels, sparse.csr_matrix):
         raise ValueError("Matrix must be CSR format.")
     # novote = [-1,0,fillonlylabel]
@@ -227,19 +225,16 @@ def mode_filter(
         rr = np.isin(nlabels.data, novote)
         nlabels.data[rr] = 0
         nlabels.eliminate_zeros()
-    # run over all rows and compute mode (maybe vectorize later)
+    # Run over all rows and compute mode.  The labels are non-negative at
+    # this point; bincount().argmax() matches scipy.stats.mode's smallest-value
+    # tie behavior without the heavy per-row SciPy dispatch.
     rempty = 0
     for row in rows:
         rvals = nlabels.data[nlabels.indptr[row] : nlabels.indptr[row + 1]]
         if rvals.size == 0:
             rempty += 1
             continue
-        # print(str(rvals))
-        mvals = mode(rvals, keepdims=True)[0]
-        # print(str(mvals))
-        if mvals.size != 0:
-            # print(str(row)+' '+str(ids[row])+' '+str(mvals[0]))
-            labels_new[ids[row]] = mvals[0]
+        labels_new[ids[row]] = np.bincount(rvals).argmax()
     if rempty > 0:
         # should not happen
         print("WARNING: row empty: " + str(rempty))

recon_surf/spherically_project.py

@@ -145,7 +145,7 @@ def get_flipped_area(tria):
         return area
 
     fem = Solver(tria, lump=False, use_cholmod=use_cholmod)
-    evals, evecs = fem.eigs(k=4)
+    evals, evecs = fem.eigs(k=4, rng=0)
 
     if debug:
         data = dict()

requirements.txt

@@ -60,7 +60,7 @@ multipledispatch==1.0.0
 narwhals==2.21.0
 networkx==3.6.1
 neurolit==0.6.1
-neuroreg==0.6.1
+neuroreg==0.6.2
 nibabel==5.4.2
 numpy==2.4.4
 packaging==26.2