diff --git a/vital/utils/image/us/measure.py b/vital/utils/image/us/measure.py
index 8b93815d..08f9984b 100644
--- a/vital/utils/image/us/measure.py
+++ b/vital/utils/image/us/measure.py
@@ -223,6 +223,84 @@ def _endo_base(
             np.array([y_coords[~coord_mask][right_point_idx], x_coords[~coord_mask][right_point_idx]]),
         )
 
+    @staticmethod
+    def _split_along_endo_axis(
+        segmentation: np.ndarray,
+        lv_labels: SemanticStructureId,
+        myo_labels: SemanticStructureId,
+        voxelspacing: Tuple[float, float] = (1, 1),
+    ) -> np.ndarray:
+        """Computes a mask that splits the image along a line between the endocardium's apex and middle of the base.
+
+        Args:
+            segmentation: (H, W), Segmentation map.
+            lv_labels: Labels of the classes that are part of the left ventricle.
+            myo_labels: Labels of the classes that are part of the left ventricle.
+            voxelspacing: Size of the segmentation's voxels along each (height, width) dimension (in mm).
+
+        Returns:
+            Mask that splits the image along a line between the endocardium's apex and middle of the base.
+        """
+        # Identify major landmarks of the left ventricle (i.e. base's corners + midpoint and apex)
+        left_corner, apex, right_corner = EchoMeasure.endo_epi_control_points(
+            segmentation, lv_labels, myo_labels, "endo", 3, voxelspacing
+        )
+        base_mid = (left_corner + right_corner) / 2
+
+        # Compute x = ay + b form of the equation of the LV center line
+        a = (apex[1] - base_mid[1]) / (apex[0] - base_mid[0])
+        b = -(a * base_mid[0] - base_mid[1])
+
+        def _is_left_of_lv_center_line(y: int, x: int) -> bool:
+            """Whether the y,x coordinates provided fall left of the LV center line."""
+            return x < a * y + b
+
+        # Create a binary mask for the whole image that is positive above the LV base's line
+        left_of_lv_center_line_mask = np.fromfunction(_is_left_of_lv_center_line, segmentation.shape)
+        return left_of_lv_center_line_mask
+
+    @staticmethod
+    @auto_cast_data
+    @batch_function(item_ndim=2)
+    def structure_area_split_by_endo_center_line(
+        segmentation: T,
+        lv_labels: SemanticStructureId,
+        myo_labels: SemanticStructureId,
+        half: Literal["left", "right"],
+        labels: SemanticStructureId = None,
+        voxelspacing: Tuple[float, float] = (1, 1),
+    ) -> T:
+        """Computes the area of a structure that falls on the left/right side of the endo center line.
+
+        Args:
+            segmentation: ([N], H, W), Segmentation map.
+            lv_labels: Labels of the classes that are part of the left ventricle.
+            myo_labels: Labels of the classes that are part of the left ventricle.
+            half: The side of the image to consider when computing the area of the structure. Either "left" or "right".
+            labels: Labels of the classes that are part of the structure for which to count the number of pixels. If
+                `None`, all truthy values will be considered part of the structure.
+            voxelspacing: Size of the segmentation's voxels along each (height, width) dimension (in mm).
+
+        Returns:
+            ([N]), Number of pixels associated with the structure that falls on the left/right side of the endo center
+            line, in each segmentation of the batch.
+        """
+        # Find the binary mask of the structure
+        if labels:
+            mask = np.isin(segmentation, labels)
+        else:
+            mask = segmentation.astype(bool)
+
+        # Find the mask of the left/right split along the endo center line
+        half_mask = EchoMeasure._split_along_endo_axis(segmentation, lv_labels, myo_labels, voxelspacing)
+        if half == "right":
+            half_mask = ~half_mask
+
+        # Only keep the part of the structure that falls on the requested side of the endo center line
+        mask = mask * half_mask
+
+        return mask.sum((-2, -1)) * (voxelspacing[0] * voxelspacing[1])
+
     @staticmethod
     @auto_cast_data
     @batch_function(item_ndim=2)
@@ -323,6 +401,7 @@ def gls(
         segmentation: T,
         lv_labels: SemanticStructureId,
         myo_labels: SemanticStructureId,
+        half: Literal["left", "right"] = None,
         voxelspacing: Tuple[float, float] = (1, 1),
     ) -> T:
         """Global Longitudinal Strain (GLS) for each frame in the sequence, compared to the first frame.
@@ -346,6 +425,24 @@ def _lv_longitudinal_length(frame: np.ndarray) -> float:
                 frame, lv_labels, functools.partial(EchoMeasure._endo_base, lv_labels=lv_labels, myo_labels=myo_labels)
             )
 
+            # Identify the apex of the endocardium, so that it can serve as the demarcation between the left/right sides
+            apex = EchoMeasure._extract_landmarks_from_polar_contour(
+                frame, lv_labels, polar_smoothing_factor=5e-2, base=False
+            )[0]
+
+            if half:
+                # Identify the point in the contour that is closest to the apex
+                apex_idx_in_contour = np.linalg.norm((contour - apex) * voxelspacing, axis=1).argmin()
+
+                # Slice the contour to only keep the points that fall on the requested side of the endo center line
+                match half:
+                    case "left":
+                        contour = contour[: apex_idx_in_contour + 1]
+                    case "right":
+                        contour = contour[apex_idx_in_contour:]
+                    case _:
+                        raise ValueError(f"Unexpected value for 'half': {half}. Use either 'left' or 'right'.")
+
             # Compute the perimeter as the sum of distances between each point along the contour and the previous one
             return sum(np.linalg.norm((p1 - p0) * voxelspacing) for p0, p1 in itertools.pairwise(contour))