diff --git a/CHANGELOG.md b/CHANGELOG.md
index 018e1424..009854e7 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -33,7 +33,6 @@ See DataLab [roadmap page](https://datalab-platform.com/en/contributing/roadmap.
 
 * Intensity profile / Segment profile extraction:
   * When extracting a profile on an image with a ROI defined, the associated PlotPy feature show a warning message ('UserWarning: Warning: converting a masked element to nan.') but the profile is correctly extracted and displayed, with NaN values where the ROI is not defined.
-  * This warning message will persist until PlotPy v2.5.2 is released
   * NaN values are now removed from the profile before plotting it
 * Simple processing features with a one-to-on mapping with a Python function (e.g. `numpy.absolute`, `numpy.log10`, etc.) and without parameters: fix result object title which was systematically ending with "|" (the character that usually precedes the list of parameters)
 * Butterworth filter: fix cutoff frequency ratio default value and valid range
diff --git a/cdl/computation/image/__init__.py b/cdl/computation/image/__init__.py
index 45fe93d0..26740325 100644
--- a/cdl/computation/image/__init__.py
+++ b/cdl/computation/image/__init__.py
@@ -12,7 +12,6 @@
 
 from __future__ import annotations
 
-import warnings
 from collections.abc import Callable
 from typing import Any, Literal
 
@@ -867,12 +866,7 @@ def compute_segment_profile(src: ImageObj, p: SegmentProfileParam) -> ImageObj:
     p.row2 = min(p.row2, data.shape[0] - 1)
     p.col2 = min(p.col2, data.shape[1] - 1)
     suffix = f"({p.row1}, {p.col1})-({p.row2}, {p.col2})"
-
-    # TODO: Remove the warning catch when upgrading to PlotPy >= 2.5.2
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", UserWarning)
-        x, y = csline(data, p.row1, p.col1, p.row2, p.col2)
-
+    x, y = csline(data, p.row1, p.col1, p.row2, p.col2)
     x, y = x[~np.isnan(y)], y[~np.isnan(y)]  # Remove NaN values
     dst = dst_11_signal(src, "segment_profile", suffix)
     dst.set_xydata(np.array(x, dtype=float), np.array(y, dtype=float))