diff --git a/fishsense_lite/commands/label_studio.py b/fishsense_lite/commands/label_studio.py
index d805a41..1ca340f 100644
--- a/fishsense_lite/commands/label_studio.py
+++ b/fishsense_lite/commands/label_studio.py
@@ -1,9 +1,8 @@
 """Module which represents the FishSense Lite Label Studio CLI."""
 
 import importlib
+import importlib.metadata
 import json
-import random
-import string
 from glob import glob
 from pathlib import Path
 from typing import List, Tuple
@@ -17,60 +16,21 @@
 from pyfishsensedev.image.image_processors import RawProcessor
 from pyfishsensedev.image.image_rectifier import ImageRectifier
 from pyfishsensedev.laser.nn_laser_detector import NNLaserDetector
-
+from pyfishsensedev.segmentation.fish.fish_segmentation_fishial_pytorch import (
+    FishSegmentationFishialPyTorch,
+)
+
+from fishsense_lite.commands.label_studio_models.laser_label_studio_json import (
+    LaserLabelStudioJSON,
+)
+from fishsense_lite.commands.label_studio_models.segmentation_label_studio_json import (
+    SegmentationLabelStudioJSON,
+)
 from fishsense_lite.utils import get_output_file, get_root, uint16_2_uint8
 
 
-class Data:
-    def __init__(self, img: str):
-        self.img = img
-
-
-class LaserValue:
-    def __init__(self, x: float, y: float, width: int, height: int):
-        self.x = x / float(width) * 100
-        self.y = y / float(height) * 100
-        self.width = 0.25
-        self.keypointlabels = ["Red Laser"]
-
-
-class LaserResult:
-    def __init__(self, laser_image_coord: np.ndarray, width: int, height: int):
-        self.original_width = width
-        self.original_height = height
-        self.image_rotation = 0
-        self.value = LaserValue(
-            laser_image_coord[0], laser_image_coord[1], width, height
-        )
-
-        letters_and_numbers = string.ascii_letters + string.digits
-
-        self.id = "".join(random.choice(letters_and_numbers) for i in range(10))
-        self.from_name = "kp-1"
-        self.to_name = "img-1"
-        self.type = "keypointlabels"
-
-
-class LaserPrediction:
-    def __init__(self, laser_image_coord: np.ndarray, width: int, height: int):
-        self.model_version = importlib.metadata.version("fishsense_lite")
-        self.result = [LaserResult(laser_image_coord, width, height)]
-
-
-class LaserLabelStudioJSON:
-    def __init__(
-        self, img: str, laser_image_coord: np.ndarray, width: int, height: int
-    ):
-        self.data = Data(img)
-        self.predictions = (
-            [LaserPrediction(laser_image_coord, width, height)]
-            if laser_image_coord is not None
-            else []
-        )
-
-
-@ray.remote(vram_mb=1536)
-def execute_laser(
+@ray.remote(vram_mb=768)
+def execute_nn_laser(
     input_file: Path,
     lens_calibration: LensCalibration,
     estimated_laser_calibration: LaserCalibration,
@@ -110,6 +70,40 @@ def execute_laser(
         laser_image_coord,
         width,
         height,
+        laser_detector.name,
+    )
+
+    with open(json_file, "w") as f:
+        f.write(json.dumps(json_objects, default=vars))
+
+
+@ray.remote(vram_mb=768)
+def execute_fishial(
+    input_file: Path, root: Path, output: Path, prefix: str, overwrite: bool
+):
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    output_file = get_output_file(input_file, root, output, "jpg")
+    json_file = output_file.with_suffix(".json")
+
+    if output_file.exists() and json_file.exists() and not overwrite:
+        return
+
+    raw_processor = RawProcessor(enable_histogram_equalization=True)
+    try:
+        image = uint16_2_uint8(raw_processor.load_and_process(input_file))
+    except:
+        return
+
+    fish_segmentation_inference = FishSegmentationFishialPyTorch(device)
+    segmentations: np.ndarray = fish_segmentation_inference.inference(image)
+
+    output_file.parent.mkdir(parents=True, exist_ok=True)
+    cv2.imwrite(output_file.absolute().as_posix(), image)
+
+    json_objects = SegmentationLabelStudioJSON(
+        f"{prefix}{output_file.relative_to(output.absolute()).as_posix()}",
+        segmentations,
+        fish_segmentation_inference.name,
     )
 
     with open(json_file, "w") as f:
@@ -242,11 +236,13 @@ def __call__(self):
 
         output = Path(self.output_path)
 
-        self.__build_laser_json(
+        self.__build_nn_laser_json(
             files, lens_calibration, estimated_laser_calibration, root, output
         )
 
-    def __build_laser_json(
+        self.__build_fishial_json(files, output)
+
+    def __build_nn_laser_json(
         self,
         files: List[Path],
         lens_calibration: LensCalibration,
@@ -254,15 +250,18 @@ def __build_laser_json(
         root: Path,
         output: Path,
     ):
-        output = output / "laser"
-        output.mkdir(parents=True, exist_ok=True)
+        laser_detector = NNLaserDetector(
+            lens_calibration, estimated_laser_calibration, "cpu"
+        )
 
-        laser_json_path = output / "label_studio.json"
-        if laser_json_path.exists() and not self.overwrite:
-            return
+        output = (
+            output
+            / f"{laser_detector.name}.{importlib.metadata.version("pyfishsensedev")}"
+        )
+        output.mkdir(parents=True, exist_ok=True)
 
         futures = [
-            execute_laser.remote(
+            execute_nn_laser.remote(
                 f,
                 lens_calibration,
                 estimated_laser_calibration,
@@ -275,3 +274,19 @@ def __build_laser_json(
         ]
 
         list(self.tqdm(futures, total=len(files)))
+
+    def __build_fishial_json(self, files: List[Path], output: Path, root: Path):
+        fish_segmentation = FishSegmentationFishialPyTorch("cpu")
+
+        output = (
+            output
+            / f"{fish_segmentation.name}.{importlib.metadata.version("pyfishsensedev")}"
+        )
+        output.mkdir(parents=True, exist_ok=True)
+
+        futures = [
+            execute_fishial.remote(f, root, output, self.prefix, self.overwrite)
+            for f in files
+        ]
+
+        list(self.tqdm(futures, total=len(files)))
diff --git a/fishsense_lite/commands/label_studio_models/data.py b/fishsense_lite/commands/label_studio_models/data.py
new file mode 100644
index 0000000..e44dcaf
--- /dev/null
+++ b/fishsense_lite/commands/label_studio_models/data.py
@@ -0,0 +1,3 @@
+class Data:
+    def __init__(self, img: str):
+        self.img = img
diff --git a/fishsense_lite/commands/label_studio_models/laser_label_studio_json.py b/fishsense_lite/commands/label_studio_models/laser_label_studio_json.py
new file mode 100644
index 0000000..f074079
--- /dev/null
+++ b/fishsense_lite/commands/label_studio_models/laser_label_studio_json.py
@@ -0,0 +1,59 @@
+import importlib
+import random
+import string
+
+import numpy as np
+
+from fishsense_lite.commands.label_studio_models.data import Data
+
+
+class LaserValue:
+    def __init__(self, x: float, y: float, width: int, height: int):
+        self.x = x / float(width) * 100
+        self.y = y / float(height) * 100
+        self.width = 0.25
+        self.keypointlabels = ["Red Laser"]
+
+
+class LaserResult:
+    def __init__(self, laser_image_coord: np.ndarray, width: int, height: int):
+        self.original_width = width
+        self.original_height = height
+        self.image_rotation = 0
+        self.value = LaserValue(
+            laser_image_coord[0], laser_image_coord[1], width, height
+        )
+
+        letters_and_numbers = string.ascii_letters + string.digits
+
+        self.id = "".join(random.choice(letters_and_numbers) for _ in range(10))
+        self.from_name = "kp-1"
+        self.to_name = "img-1"
+        self.type = "keypointlabels"
+
+
+class LaserPrediction:
+    def __init__(
+        self, laser_image_coord: np.ndarray, width: int, height: int, model_name: str
+    ):
+        self.model_version = (
+            f"{model_name}.{importlib.metadata.version("pyfishsensedev")}"
+        )
+        self.result = [LaserResult(laser_image_coord, width, height)]
+
+
+class LaserLabelStudioJSON:
+    def __init__(
+        self,
+        img: str,
+        laser_image_coord: np.ndarray,
+        width: int,
+        height: int,
+        model_name: str,
+    ):
+        self.data = Data(img)
+        self.predictions = (
+            [LaserPrediction(laser_image_coord, width, height, model_name)]
+            if laser_image_coord is not None
+            else []
+        )
diff --git a/fishsense_lite/commands/label_studio_models/segmentation_label_studio_json.py b/fishsense_lite/commands/label_studio_models/segmentation_label_studio_json.py
new file mode 100644
index 0000000..4b7a577
--- /dev/null
+++ b/fishsense_lite/commands/label_studio_models/segmentation_label_studio_json.py
@@ -0,0 +1,220 @@
+import importlib
+import random
+import string
+
+import numpy as np
+
+from fishsense_lite.commands.label_studio_models.data import Data
+
+
+# Adapted from https://github.com/HumanSignal/label-studio-converter/blob/master/label_studio_converter/brush.py
+class SegmentationValue:
+    def __init__(self, mask: np.ndarray):
+        self.format = "rle"
+        self.brushlabels = ["Fish"]
+        self.rle = self.__mask2rle(self.__convert_mask(mask))
+
+    def __convert_mask(self, mask: np.ndarray):
+        unique_values = np.unique_values(mask[mask != 0])
+
+        converted_mask = np.zeros_like(mask)
+        for value in unique_values:
+            converted_mask[mask == value] = 1
+
+        return converted_mask
+
+    def __mask2rle(self, mask: np.ndarray):
+        """Convert mask to RLE
+
+        :param mask: uint8 or int np.array mask with len(shape) == 2 like grayscale image
+        :return: list of ints in RLE format
+        """
+        assert len(mask.shape) == 2, "mask must be 2D np.array"
+        assert mask.dtype == np.uint8 or mask.dtype == int, "mask must be uint8 or int"
+        array = mask.ravel()
+        array = np.repeat(array, 4)  # must be 4 channels
+        rle = self.__encode_rle(array)
+        return rle
+
+    def __encode_rle(self, arr: np.ndarray, wordsize=8, rle_sizes=[3, 4, 8, 16]):
+        """Encode a 1d array to rle
+
+
+        :param arr: flattened np.array from a 4d image (R, G, B, alpha)
+        :type arr: np.array
+        :param wordsize: wordsize bits for decoding, default is 8
+        :type wordsize: int
+        :param rle_sizes:  list of ints which state how long a series is of the same number
+        :type rle_sizes: list
+        :return rle: run length encoded array
+        :type rle: list
+
+        """
+        # Set length of array in 32 bits
+        num = len(arr)
+        numbits = f"{num:032b}"
+
+        # put in the wordsize in bits
+        wordsizebits = f"{wordsize - 1:05b}"
+
+        # put rle sizes in the bits
+        rle_bits = "".join([f"{x - 1:04b}" for x in rle_sizes])
+
+        # combine it into base string
+        base_str = numbits + wordsizebits + rle_bits
+
+        # start with creating the rle bite string
+        out_str = ""
+        for length_reeks, p, value in zip(*self.__base_rle_encode(arr)):
+            # TODO: A nice to have but --> this can be optimized but works
+            if length_reeks == 1:
+                # we state with the first 0 that it has a length of 1
+                out_str += "0"
+                # We state now the index on the rle sizes
+                out_str += "00"
+
+                # the rle size value is 0 for an individual number
+                out_str += "000"
+
+                # put the value in a 8 bit string
+                out_str += f"{value:08b}"
+                state = "single_val"
+
+            elif length_reeks > 1:
+                state = "series"
+                # rle size = 3
+                if length_reeks <= 8:
+                    # Starting with a 1 indicates that we have started a series
+                    out_str += "1"
+
+                    # index in rle size arr
+                    out_str += "00"
+
+                    # length of array to bits
+                    out_str += f"{length_reeks - 1:03b}"
+
+                    out_str += f"{value:08b}"
+
+                # rle size = 4
+                elif 8 < length_reeks <= 16:
+                    # Starting with a 1 indicates that we have started a series
+                    out_str += "1"
+                    out_str += "01"
+
+                    # length of array to bits
+                    out_str += f"{length_reeks - 1:04b}"
+
+                    out_str += f"{value:08b}"
+
+                # rle size = 8
+                elif 16 < length_reeks <= 256:
+                    # Starting with a 1 indicates that we have started a series
+                    out_str += "1"
+
+                    out_str += "10"
+
+                    # length of array to bits
+                    out_str += f"{length_reeks - 1:08b}"
+
+                    out_str += f"{value:08b}"
+
+                # rle size = 16 or longer
+                else:
+                    length_temp = length_reeks
+                    while length_temp > 2**16:
+                        # Starting with a 1 indicates that we have started a series
+                        out_str += "1"
+
+                        out_str += "11"
+                        out_str += f"{2 ** 16 - 1:016b}"
+
+                        out_str += f"{value:08b}"
+                        length_temp -= 2**16
+
+                    # Starting with a 1 indicates that we have started a series
+                    out_str += "1"
+
+                    out_str += "11"
+                    # length of array to bits
+                    out_str += f"{length_temp - 1:016b}"
+
+                    out_str += f"{value:08b}"
+
+        # make sure that we have an 8 fold lenght otherwise add 0's at the end
+        nzfill = 8 - len(base_str + out_str) % 8
+        total_str = base_str + out_str
+        total_str = total_str + nzfill * "0"
+
+        rle = self.__bits2byte(total_str)
+
+        return rle
+
+    # Shamelessly plagiarized from https://stackoverflow.com/a/32681075/6051733
+    def __base_rle_encode(self, inarray: np.ndarray):
+        """run length encoding. Partial credit to R rle function.
+        Multi datatype arrays catered for including non Numpy
+        returns: tuple (runlengths, startpositions, values)"""
+        ia = np.asarray(inarray)  # force numpy
+        n = len(ia)
+        if n == 0:
+            return None, None, None
+        else:
+            y = ia[1:] != ia[:-1]  # pairwise unequal (string safe)
+            i = np.append(np.where(y), n - 1)  # must include last element posi
+            z = np.diff(np.append(-1, i))  # run lengths
+            p = np.cumsum(np.append(0, z))[:-1]  # positions
+            return z, p, ia[i]
+
+    def __bits2byte(self, arr_str, n=8):
+        """Convert bits back to byte
+
+        :param arr_str:  string with the bit array
+        :type arr_str: str
+        :param n: number of bits to separate the arr string into
+        :type n: int
+        :return rle:
+        :type rle: list
+        """
+        rle = []
+        numbers = [arr_str[i : i + n] for i in range(0, len(arr_str), n)]
+        for i in numbers:
+            rle.append(int(i, 2))
+        return rle
+
+
+class SegmentationResult:
+    def __init__(self, mask: np.ndarray):
+        height, width = mask.shape
+
+        self.original_width = width
+        self.original_height = height
+        self.image_rotation = 0
+        self.value = SegmentationValue(mask)
+
+        letters_and_numbers = string.ascii_letters + string.digits
+
+        self.id = "".join(random.choice(letters_and_numbers) for _ in range(10))
+        self.from_name = "kp-1"
+        self.to_name = "img-1"
+        self.type = "keypointlabels"
+
+
+class SegmentationPrediction:
+    def __init__(self, mask: np.ndarray, model_name: str):
+        self.model_version = (
+            f"{model_name}.{importlib.metadata.version("pyfishsensedev")}"
+        )
+        self.result = [SegmentationResult(mask)]
+
+
+class SegmentationLabelStudioJSON:
+    def __init__(
+        self,
+        img: str,
+        mask: np.ndarray,
+        model_name: str,
+    ):
+        self.data = Data(img)
+        self.predictions = (
+            [SegmentationPrediction(mask, model_name)] if mask.sum() > 0 else []
+        )
diff --git a/poetry.lock b/poetry.lock
index de298f2..ffce5b8 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -404,76 +404,65 @@ test = ["pytest"]
 
 [[package]]
 name = "contourpy"
-version = "1.3.0"
+version = "1.3.1"
 description = "Python library for calculating contours of 2D quadrilateral grids"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
 files = [
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 ]
 
 [package.dependencies]
@@ -1064,6 +1053,17 @@ MarkupSafe = ">=2.0"
 [package.extras]
 i18n = ["Babel (>=2.7)"]
 
+[[package]]
+name = "joblib"
+version = "1.4.2"
+description = "Lightweight pipelining with Python functions"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
 [[package]]
 name = "jsonschema"
 version = "4.23.0"
@@ -2272,6 +2272,7 @@ opencv-python = "^4.10.0.84"
 pymupdf = "^1.24.10"
 rawpy = "^0.22.0"
 scikit-image = "^0.24.0"
+scikit-learn = "^1.5.2"
 shapely = "^2.0.6"
 torch = "^2.4.0"
 torchvision = "^0.19.0"
@@ -2280,7 +2281,7 @@ torchvision = "^0.19.0"
 type = "git"
 url = "https://github.com/UCSD-E4E/pyFishSenseDev"
 reference = "main"
-resolved_reference = "d11afcb02c31eac29f60e9fbfc6073035ea2f057"
+resolved_reference = "185f08d1ea1e44b21b2970ee1362490ed96180c2"
 
 [[package]]
 name = "pygments"
@@ -2872,6 +2873,56 @@ docs = ["PyWavelets (>=1.1.1)", "dask[array] (>=2022.9.2)", "ipykernel", "ipywid
 optional = ["PyWavelets (>=1.1.1)", "SimpleITK", "astropy (>=5.0)", "cloudpickle (>=0.2.1)", "dask[array] (>=2021.1.0)", "matplotlib (>=3.6)", "pooch (>=1.6.0)", "pyamg", "scikit-learn (>=1.1)"]
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+[[package]]
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+description = "A set of python modules for machine learning and data mining"
+optional = false
+python-versions = ">=3.9"
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+numpy = ">=1.19.5"
+scipy = ">=1.6.0"
+threadpoolctl = ">=3.1.0"
+
+[package.extras]
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+maintenance = ["conda-lock (==2.5.6)"]
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+
 [[package]]
 name = "scipy"
 version = "1.14.1"
@@ -3059,6 +3110,17 @@ mpmath = ">=1.1.0,<1.4"
 [package.extras]
 dev = ["hypothesis (>=6.70.0)", "pytest (>=7.1.0)"]
 
+[[package]]
+name = "threadpoolctl"
+version = "3.5.0"
+description = "threadpoolctl"
+optional = false
+python-versions = ">=3.8"
+files = [
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+]
+
 [[package]]
 name = "tifffile"
 version = "2024.9.20"