diff --git a/brainglobe_template_builder/find_midline.py b/brainglobe_template_builder/find_midline.py
deleted file mode 100644
index 1a70d04..0000000
--- a/brainglobe_template_builder/find_midline.py
+++ /dev/null
@@ -1,112 +0,0 @@
-"""
-This module is for finding the midline plane of a 3D image stack.
-
-The image is loaded into napari, and the user is asked to annotate
-at least 3 points on the midline. The midline plane is then fitted
-to these points using a least squares regression. Finally, the
-transformation matrix needed to rotate the image so that the midline
-plane is centered is calculated and saved.
-"""
-
-# %%
-from itertools import product
-from pathlib import Path
-
-import imio
-import napari
-import numpy as np
-import pandas as pd
-import skimage
-
-# %%
-# load tiff image into napari
-data_dir = Path(
-    "/Users/nsirmpilatze/Data/BlackCap/ants/template_v2/results_tiff"
-)
-image_path = data_dir / "template.tif"
-image = imio.load_any(image_path.as_posix())
-
-viewer = napari.view_image(image, name="image")
-
-# %%
-# Segment the brain from the background via thresholding
-# and add brain mask to napari viewer
-
-# first apply gaussian filter to image
-image_smoothed = skimage.filters.gaussian(image, sigma=3)
-viewer.add_image(
-    image_smoothed,
-    name="smoothed_image",
-    visible=False,
-)
-
-# %%
-# then apply thresholding to smoothed image
-thresholded = skimage.filters.threshold_triangle(image_smoothed)
-binary_smoothed = image_smoothed > thresholded
-# Remove small objects
-min_object_size = 500  # Define a minimum object size to keep (in pixels)
-binary_smoothed = skimage.morphology.remove_small_objects(
-    binary_smoothed, min_size=min_object_size
-)
-viewer.add_image(
-    binary_smoothed,
-    name="initial_binary",
-    colormap="green",
-    blending="additive",
-    opacity=0.5,
-)
-
-# %%
-# Erode the binary mask to remove the edges of the brain
-# and the resulting mask to napari viewer as a label layer
-eroded = skimage.morphology.binary_erosion(
-    binary_smoothed, footprint=np.ones((5, 5, 5))
-)
-viewer.add_labels(
-    eroded,
-    name="brain_mask",
-    opacity=0.5,
-)
-viewer.layers["initial_binary"].visible = False
-viewer.layers["brain_mask"].selected_label = 1
-
-
-# %%
-# Define initial set of 9 midline points
-
-# Find slices at 1/4, 2/4, and 3/4 of each dimension
-slices = [int(dim / 4) * i for dim in image.shape for i in [1, 2, 3]]
-z_slices, y_slices, x_slices = slices[0:3], slices[3:6], slices[6:9]
-# Find points at the intersection the middle x slice with the y and z slices
-grid_points = np.array(list(product(z_slices, y_slices, [x_slices[1]])))
-print(grid_points)
-
-# %%
-# Add points to napari viewer
-points_layer = viewer.add_points(
-    grid_points,
-    name="midline",
-    face_color="#ffaa00",
-    size=5,
-    opacity=0.5,
-    edge_color="#ff0000",
-    edge_width=1.5,
-    edge_width_is_relative=False,
-)
-# %%
-# Go to the first z slice
-
-# activate point layer
-viewer.layers.selection.active_layer = points_layer
-# change selection mode to select
-viewer.layers.selection.mode = "select"
-# go to the first z slice
-viewer.dims.set_point(0, z_slices[0])
-
-# %%
-# save points to csv
-midline_points = points_layer.data
-midline_points = pd.DataFrame(midline_points, columns=["z", "y", "x"])
-points_path = data_dir / "midline_points.csv"
-midline_points.to_csv(points_path, index=False)
diff --git a/brainglobe_template_builder/io.py b/brainglobe_template_builder/io.py
index 7397b81..7a06b17 100644
--- a/brainglobe_template_builder/io.py
+++ b/brainglobe_template_builder/io.py
@@ -2,6 +2,7 @@
 
 import imio
 import numpy as np
+import pandas as pd
 
 
 def load_image_to_napari(tiff_path: Path):
@@ -27,6 +28,25 @@ def load_image_to_napari(tiff_path: Path):
     return image
 
 
+def save_3d_points_to_csv(points: np.ndarray, file_path: Path):
+    """
+    Save 3D points to a csv file
+    """
+
+    if points.shape[1] != 3:
+        raise ValueError(
+            f"Points must be of shape (n, 3). Got shape {points.shape}"
+        )
+    if file_path.suffix != ".csv":
+        raise ValueError(
+            f"File extension {file_path.suffix} is not valid. "
+            f"Expected file path to end in .csv"
+        )
+
+    points_df = pd.DataFrame(points, columns=["z", "y", "x"])
+    points_df.to_csv(file_path, index=False)
+
+
 def save_nii(stack: np.ndarray, pix_sizes: list, dest_path: Path):
     """
     Save 3D image stack to dest_path as a nifti image.
diff --git a/brainglobe_template_builder/napari/__init__.py b/brainglobe_template_builder/napari/__init__.py
index 3bd5bae..9774bc0 100644
--- a/brainglobe_template_builder/napari/__init__.py
+++ b/brainglobe_template_builder/napari/__init__.py
@@ -7,9 +7,13 @@
     __version__ = "unknown"
 
 from brainglobe_template_builder.napari._reader import napari_get_reader
-from brainglobe_template_builder.napari._widget import mask_widget
+from brainglobe_template_builder.napari._widget import (
+    mask_widget,
+    points_widget,
+)
 
 __all__ = (
     "napari_get_reader",
     "mask_widget",
+    "points_widget",
 )
diff --git a/brainglobe_template_builder/napari/_widget.py b/brainglobe_template_builder/napari/_widget.py
index 1279fb7..9ebae3e 100644
--- a/brainglobe_template_builder/napari/_widget.py
+++ b/brainglobe_template_builder/napari/_widget.py
@@ -10,15 +10,31 @@
 
 import numpy as np
 from magicgui import magic_factory
-from napari.layers import Image
+from magicgui.widgets import ComboBox, Container
+from napari import Viewer
+from napari.layers import Image, Labels, Points
 from napari.types import LayerDataTuple
 from napari_plugin_engine import napari_hook_implementation
 
 from brainglobe_template_builder.utils import (
     extract_largest_object,
+    get_midline_points,
     threshold_image,
 )
 
+# 9 colors taken from ColorBrewer2.org Set3 palette
+POINTS_COLOR_CYCLE = [
+    "#8dd3c7",
+    "#ffffb3",
+    "#bebada",
+    "#fb8072",
+    "#80b1d3",
+    "#fdb462",
+    "#b3de69",
+    "#fccde5",
+    "#d9d9d9",
+]
+
 
 @magic_factory(
     call_button="generate mask",
@@ -55,10 +71,8 @@ def mask_widget(
 
     Returns
     -------
-    layers : list[LayerDataTuple]
-        A list of napari layers to add to the viewer.
-        The first layer is the mask, and the second layer is the smoothed
-        image (if smoothing was applied).
+    napari.types.LayerDataTuple
+        A napari Labels layer containing the mask.
     """
 
     if image is not None:
@@ -91,6 +105,90 @@ def mask_widget(
     return (mask, {"name": f"Mask_{image.name}", "opacity": 0.5}, "labels")
 
 
+def create_point_label_menu(
+    points_layer: Points, point_labels: list[str]
+) -> Container:
+    """Create a point label menu widget for a napari points layer.
+
+    Parameters:
+    -----------
+    points_layer : napari.layers.Points
+        a napari points layer
+    point_labels : list[str]
+        a list of point labels
+
+    Returns:
+    --------
+    label_menu : Container
+        the magicgui Container with a dropdown menu widget
+    """
+    # Create the label selection menu
+    label_menu = ComboBox(label="point_label", choices=point_labels)
+    label_widget = Container(widgets=[label_menu])
+
+    def update_label_menu(event):
+        """Update the label menu when the point selection changes"""
+        new_label = str(points_layer.current_properties["label"][0])
+        if new_label != label_menu.value:
+            label_menu.value = new_label
+
+    points_layer.events.current_properties.connect(update_label_menu)
+
+    def label_changed(event):
+        """Update the Points layer when the label menu selection changes"""
+        selected_label = event.value
+        current_properties = points_layer.current_properties
+        current_properties["label"] = np.asarray([selected_label])
+        points_layer.current_properties = current_properties
+
+    label_menu.changed.connect(label_changed)
+
+    return label_widget
+
+
+@magic_factory(
+    call_button="Estimate midline points",
+)
+def points_widget(
+    mask: Labels,
+) -> LayerDataTuple:
+    """Create a points layer with 9 midline points.
+
+    Parameters
+    ----------
+    mask : Labels
+        A napari labels layer to use as a reference for the points.
+
+    Returns
+    -------
+    napari.types.LayerDataTuple
+        A napari Points layer containing the estimated midline points.
+    """
+
+    # Estimate 9 midline points
+    points = get_midline_points(mask.data)
+
+    point_labels = np.arange(1, points.shape[0] + 1)
+
+    point_attrs = {
+        "properties": {"label": point_labels},
+        "edge_color": "label",
+        "edge_color_cycle": POINTS_COLOR_CYCLE,
+        "symbol": "o",
+        "face_color": "transparent",
+        "edge_width": 0.3,
+        "size": 8,
+        "ndim": mask.ndim,
+        "name": "midline points",
+    }
+
+    return (points, point_attrs, "points")
+
+
 @napari_hook_implementation
 def napari_experimental_provide_dock_widget():
-    return mask_widget
+    return [mask_widget, points_widget]
+
+
+viewer = Viewer()
+viewer.add_points()
diff --git a/brainglobe_template_builder/napari/napari.yaml b/brainglobe_template_builder/napari/napari.yaml
index a993f6e..db86ce4 100644
--- a/brainglobe_template_builder/napari/napari.yaml
+++ b/brainglobe_template_builder/napari/napari.yaml
@@ -7,7 +7,10 @@ contributions:
       title: Open data with brainglobe-template-builder
     - id: brainglobe-template-builder.make_mask_widget
       python_name: brainglobe_template_builder.napari._widget:mask_widget
-      title: Generate organ mask
+      title: Generate mask
+    - id: brainglobe-template-builder.make_points_widget
+      python_name: brainglobe_template_builder.napari._widget:points_widget
+      title: Annotate points
   readers:
     - command: brainglobe-template-builder.get_reader
       accepts_directories: false
@@ -17,3 +20,5 @@ contributions:
   widgets:
     - command: brainglobe-template-builder.make_mask_widget
       display_name: Generate Mask
+    - command: brainglobe-template-builder.make_points_widget
+      display_name: Annotate Points
diff --git a/brainglobe_template_builder/utils.py b/brainglobe_template_builder/utils.py
index d6653fc..65ac624 100644
--- a/brainglobe_template_builder/utils.py
+++ b/brainglobe_template_builder/utils.py
@@ -1,3 +1,4 @@
+from itertools import product
 from typing import Literal, Union
 
 import numpy as np
@@ -54,3 +55,46 @@ def threshold_image(
         return image > thresholded
     else:
         raise ValueError(f"Unknown thresholding method {method}")
+
+
+def get_midline_points(mask: np.ndarray):
+    """Get a set of 9 points roughly on the x axis midline of a 3D binary mask.
+
+    Parameters
+    ----------
+    mask : np.ndarray
+        A binary mask of shape (z, y, x).
+
+    Returns
+    -------
+    np.ndarray
+        An array of shape (9, 3) containing the midline points.
+    """
+
+    # Ensure mask is 3D
+    if mask.ndim != 3:
+        raise ValueError("Mask must be 3D")
+
+    # Ensure mask is binary
+    try:
+        mask = mask.astype(bool)
+    except ValueError:
+        raise ValueError("Mask must be binary")
+
+    # Derive mask properties
+    props = measure.regionprops(measure.label(mask))[0]
+    # bbox in shape (3, 2): for each dim (row) the min and max (col)
+    bbox = np.array(props.bbox).reshape(2, 3).T
+    bbox_ranges = bbox[:, 1] - bbox[:, 0]
+    # mask centroid in shape (3,)
+    centroid = np.array(props.centroid)
+
+    # Find slices at 1/4, 2/4, and 3/4 of the z and y dimensions
+    z_slices = [bbox_ranges[0] / 4 * i for i in [1, 2, 3]]
+    y_slices = [bbox_ranges[1] / 4 * i for i in [1, 2, 3]]
+    # Find points at the intersection the centroid's x slice
+    # with the above y and z slices.
+    # This produces a set of 9 points roughly on the midline
+    points = list(product(z_slices, y_slices, [centroid[2]]))
+
+    return np.array(points)