diff --git a/essentials.py b/essentials.py
index 22f5c14..4e4372a 100644
--- a/essentials.py
+++ b/essentials.py
@@ -603,6 +603,58 @@ class MaskFromColor:
 
         return (mask, )
 
+class MaskFromSegmentation:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE", ),
+                "segments": ("INT", { "default": 6, "min": 1, "max": 16, "step": 1, }),
+                "remove_isolated_pixels": ("INT", { "default": 0, "min": 0, "max": 32, "step": 1, }),
+                "remove_small_masks": ("FLOAT", { "default": 0.0, "min": 0., "max": 1., "step": 0.01, }),
+                "fill_holes": ("BOOLEAN", { "default": False }),
+            }
+        }
+
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "execute"
+    CATEGORY = "essentials"
+
+    def execute(self, image, segments, remove_isolated_pixels, fill_holes, remove_small_masks):
+        im = image[0] # we only work on the first image in the batch
+        im = Image.fromarray((im * 255).to(torch.uint8).cpu().numpy(), mode="RGB")
+        im = im.quantize(palette=im.quantize(colors=segments), dither=Image.Dither.NONE)       
+        im = torch.tensor(np.array(im.convert("RGB"))).float() / 255.0
+
+        colors = im.reshape(-1, im.shape[-1])
+        colors = torch.unique(colors, dim=0)
+
+        masks = []
+        for color in colors:
+            mask = (im == color).all(dim=-1).float()
+            # remove isolated pixels
+            if remove_isolated_pixels > 0:
+                mask_np = mask.cpu().numpy()
+                mask_np = scipy.ndimage.binary_opening(mask_np, structure=np.ones((remove_isolated_pixels, remove_isolated_pixels)))
+                mask = torch.from_numpy(mask_np)
+
+            # fill holes
+            if fill_holes:
+                mask_np = mask.cpu().numpy()
+                mask_np = scipy.ndimage.binary_fill_holes(mask_np)
+                mask = torch.from_numpy(mask_np)
+
+            # if the mask is too small, it's probably noise
+            if mask.sum() / (mask.shape[0]*mask.shape[1]) > remove_small_masks:
+                masks.append(mask)
+
+        if masks == []:
+            masks.append(torch.zeros_like(im).squeeze(-1).unsqueeze(0)) # return an empty mask if no masks were found, prevents errors
+
+        mask = torch.stack(masks, dim=0).float()
+
+        return (mask, )
+
 class MaskFromBatch:
     @classmethod
     def INPUT_TYPES(s):
@@ -1687,6 +1739,7 @@ NODE_CLASS_MAPPINGS = {
     "MaskFromColor+": MaskFromColor,
     "MaskFromBatch+": MaskFromBatch,
     "MaskBoundingBox+": MaskBoundingBox,
+    "MaskFromSegmentation+": MaskFromSegmentation,
 
     "SimpleMath+": SimpleMath,
     "ConsoleDebug+": ConsoleDebug,
@@ -1737,6 +1790,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "MaskFromColor+": "🔧 Mask From Color",
     "MaskFromBatch+": "🔧 Mask From Batch",
     "MaskBoundingBox+": "🔧 Mask Bounding Box",
+    "MaskFromSegmentation+": "🔧 Mask From Segmentation",
 
     "SimpleMath+": "🔧 Simple Math",
     "ConsoleDebug+": "🔧 Console Debug",