diff --git a/dacapo/experiments/datasplits/datasets/arrays/__init__.py b/dacapo/experiments/datasplits/datasets/arrays/__init__.py
index d8e6d6d7..7b16453b 100644
--- a/dacapo/experiments/datasplits/datasets/arrays/__init__.py
+++ b/dacapo/experiments/datasplits/datasets/arrays/__init__.py
@@ -6,7 +6,6 @@
 from .binarize_array_config import BinarizeArrayConfig  # noqa
 from .resampled_array_config import ResampledArrayConfig  # noqa
 from .intensity_array_config import IntensitiesArrayConfig  # noqa
-from .missing_annotations_mask_config import MissingAnnotationsMaskConfig  # noqa
 from .ones_array_config import OnesArrayConfig  # noqa
 from .concat_array_config import ConcatArrayConfig  # noqa
 from .logical_or_array_config import LogicalOrArrayConfig  # noqa
diff --git a/dacapo/experiments/datasplits/datasets/arrays/missing_annotations_mask_config.py b/dacapo/experiments/datasplits/datasets/arrays/missing_annotations_mask_config.py
deleted file mode 100644
index ba805b64..00000000
--- a/dacapo/experiments/datasplits/datasets/arrays/missing_annotations_mask_config.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import attr
-
-from .array_config import ArrayConfig
-
-from typing import List, Tuple
-from funlib.persistence import Array
-from fibsem_tools.metadata.groundtruth import LabelList
-
-import dask.array as da
-
-
-@attr.s
-class MissingAnnotationsMaskConfig(ArrayConfig):
-    """
-    This config class provides the necessary configuration for turning an Annotated dataset into a
-    multi class binary classification problem
-
-    Attributes:
-        source_array_config : ArrayConfig
-            The Array from which to pull annotated data. Is expected to contain a volume with uint64 voxels and no channel dimension
-        groupings : List[Tuple[str, List[int]]]
-            List of id groups with a symantic name. Each id group is a List of ids.
-            Group i found in groupings[i] will be binarized and placed in channel i.
-    Note:
-        The output array will have a channel dimension equal to the number of groups.
-        Each channel will be a binary mask of the ids in the groupings list.
-    """
-
-    source_array_config: ArrayConfig = attr.ib(
-        metadata={
-            "help_text": "The Array from which to pull annotated data. Is expected to contain a volume with uint64 voxels and no channel dimension"
-        }
-    )
-
-    groupings: List[Tuple[str, List[int]]] = attr.ib(
-        metadata={
-            "help_text": "List of id groups with a symantic name. Each id group is a List of ids. "
-            "Group i found in groupings[i] will be binarized and placed in channel i."
-        }
-    )
-
-    def array(self, mode: str = "r") -> Array:
-        labels = self.source_array_config.array(mode)
-        grouped = da.ones((len(self.groupings), *labels.shape), dtype=bool)
-        grouped[:] = labels.data > 0
-        labels_list = LabelList.parse_obj(
-            {"labels": labels._source_data.attrs["labels"]}
-        ).labels
-        present_not_annotated = set(
-            [
-                label.value
-                for label in labels_list
-                if label.annotationState.present and not label.annotationState.annotated
-            ]
-        )
-        for i, (_, ids) in enumerate(self.groupings):
-            if any([id in present_not_annotated for id in ids]):
-                grouped[i] = 0
-
-        return Array(
-            grouped, labels.offset, labels.voxel_size, labels.axis_names, labels.units
-        )
diff --git a/pyproject.toml b/pyproject.toml
index 9070e4b3..cd65da23 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -38,7 +38,6 @@ dependencies = [
         "lazy-property",
         "neuroglancer",
         "torch",
-        "fibsem_tools >= 6.0, <=6.3",
         "attrs",
         "bokeh",
         "numpy-indexed>=0.3.7",
@@ -183,7 +182,6 @@ module = [
     "daisy.*",
     "lazy_property.*",
     "skimage.*",
-    "fibsem_tools.*",
     "neuroglancer.*",
     "tqdm.*",
     "zarr.*",