Create LyNoS.py

LyNoS.py

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+"""LyNoS: Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding."""
+
+
+import datasets
+
+_DESCRIPTION = """\
+LyNoS: Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding.
+"""
+
+_HOMEPAGE = "https://github.com/raidionics/LyNoS"
+
+_LICENSE = "MIT"
+
+_CITATION = """\
+@article{bouget2023mediastinal,
+  title={Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding},
+  author={Bouget, David and Pedersen, Andr{\'e} and Vanel, Johanna and Leira, Haakon O and Lang{\o}, Thomas},
+  journal={Computer Methods in Biomechanics and Biomedical Engineering: Imaging \& Visualization},
+  volume={11},
+  number={1},
+  pages={44--58},
+  year={2023},
+  publisher={Taylor \& Francis}
+}
+
+"""
+
+_URLS = [
+    {
+        "ct": f"data/Pat{i}/Pat{i}_data.nii.gz",
+        "azygos": f"data/Pat{i}/Pat{i}_labels_Azygos.nii.gz",
+        "brachiocephalicveins": f"data/Pat{i}/Pat{i}_labels_BrachiocephalicVeins.nii.gz",
+        "esophagus": f"data/Pat{i}/Pat{i}_labels_Esophagus.nii.gz",
+        "lymphnodes": f"data/Pat{i}/Pat{i}_labels_LymphNodes.nii.gz",
+        "subclaviancarotidarteries": f"data/Pat{i}/Pat{i}_labels_SubCarArt.nii.gz",
+    }
+    for i in range(1, 15)
+]
+
+
+class LyNoS(datasets.GeneratorBasedBuilder):
+    """A segmentation benchmark dataset for enlarged lymph nodes in patients with primary lung cancer."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "ct": datasets.Value("string"),
+                "lymphnodes": datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dirs = dl_manager.download(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "data_dirs": data_dirs,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_dirs):
+        for key, patient in enumerate(data_dirs):
+            yield key, patient