From 19dcca89b3ff0dfae9399e98618b176ad5715f7f Mon Sep 17 00:00:00 2001
From: mattia-wsl <mat.ronzoni@gmail.com>
Date: Fri, 9 Apr 2021 11:03:23 +0200
Subject: [PATCH 1/2] changes 9.4

---
 Pipfile      |   3 +-
 Pipfile.lock | 548 +++++++++++++++++++++++++++++++--------------------
 run.py       |  54 +++--
 3 files changed, 369 insertions(+), 236 deletions(-)

diff --git a/Pipfile b/Pipfile
index 2fbc235..201e49b 100644
--- a/Pipfile
+++ b/Pipfile
@@ -16,8 +16,7 @@ SimpleITK="*"
 tqdm="*"
 fill_voids="*"
 zipfile36="*"
+vtk = "*"
 
 [requires]
 python_version = "3.8"
-
-
diff --git a/Pipfile.lock b/Pipfile.lock
index 27ae0c0..c88a3ac 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "1e9aa47a2b74fea92f38a9bebd5a3e2ccda0c06f3225409a6533773410e4b7a9"
+            "sha256": "1c39ea75a396b2fc28437ffe02d028ab5e795fa85d9c7f4c4544e99e49e4a759"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -32,168 +32,241 @@
         },
         "fastremap": {
             "hashes": [
-                "sha256:035bd3e244618aa9331ac83043c2d5ee430023b8a86ba8d16f80e7104cf524c8",
-                "sha256:1f7398bf5db1578568c55dde79425d8abf8c2cfee3aaf23af5070ee205a0fed9",
-                "sha256:2ed91854f54b0747e2527c362b8e77f68290e9cb69fc45bc9aaeeb7f5a57d4a9",
-                "sha256:37bbbf43531edad99ae90f910e8a5ae4ab4f097101851b8862aebe98937430ea",
-                "sha256:5bff94e951c1945954e7b73c6ceac50fca6bf39f0babf320b3d3e2fbeedea9d2",
-                "sha256:713442445414b44afdb0ad11f17a5fedcb60f001aff851d1805573211a94b3db",
-                "sha256:7a40520ff29c027fca00611ba22a21bb42dd5601e6ad9b2cb8e90b16db3fdf25",
-                "sha256:83137f0a1136122b7d76e87cff6f408e3097b55a3244eaad39359f2d8cc26d12",
-                "sha256:85bb84e15a1fb0047c7b3952d84dca1aa7f1e7e1f0634b339402ef780d96164b",
-                "sha256:87c4996c2ebc61daf7610509f99263bbc42b03abf083d0ecb113563ba0be4eeb",
-                "sha256:9091b82cf68977c6998bad5becaafe0567f43fdbf0874ce91e81b9d79841af78",
-                "sha256:a7bf838a74131538eb728e68692ad8e14db8d4cad888344f64d2260b0711b041",
-                "sha256:b2be01f4fd99e680b30e22134c67b3ce7e55b3da07697ce44e4c9297c55d40d8",
-                "sha256:c0a1a05700c5d9f840fe757160fd29bc3228ba4de2f92e0aa6519cb7f661fc9a",
-                "sha256:de6f8db8749af6700d870a97d901b6ce57e38e40167ac5cabe60b60e96c55b30",
-                "sha256:ff646a9ab5b920b8808b0db090612ca5c7b573d1032cd74c0925af5652435a4b",
-                "sha256:ff94bd18ab4085ee148012a1800a656e91d3c251c5eb49fc559873cf7400190d"
+                "sha256:0a22e8b160b7576b44c2947fea71b9b82465221145038ee4d6e1be4c848dc59f",
+                "sha256:195e5268b63d875bb607cbf5900e60666f78aabfe2b4136d805e6f15481069d1",
+                "sha256:1c97b6230978a2bbb08666006bae9eb5c82374ef23b71d0eaa0a904b8719c0cb",
+                "sha256:259864f5f38e04f404344a65464ceecb1ddd85893e8b3459fa9f15de87fc81ed",
+                "sha256:2fdb638045333235166c686e1cea1b6e735af0505945690f64299a1fcaad1440",
+                "sha256:31469fe897314d92e221f0ce9c55cb691feff1c1a3695d057ae4562edcfa1111",
+                "sha256:519b282a133b6f91a13a36d48d14e8d37073774e29ae899cab87053c6102f19f",
+                "sha256:53bcfb9515fc01c6bf106e1852808ffd5a7ea42159a392737f5501236019beb6",
+                "sha256:5bf7fe1ddbfca401d409413a6844665f547a55c9473a31c575bf6d8ac5ec1e4f",
+                "sha256:5cd7de35adc1ea858f6aab2cbae120f4ec239c3471d31228244164446accc292",
+                "sha256:63b7956c75bd86deab94a2c21cfd8d1f4b87fc87f23fc7d87286c10ba8893a97",
+                "sha256:649f72de39564f0963c0bf5ed5398a6de4ce3bbc1355047b5c1ae3ffd6a85ad3",
+                "sha256:6cb6d026a1a5b4af1be0363f7fd8f6f2e26ca37143a4d0c5ecc6a517f1ee29ec",
+                "sha256:6fd516f741eb0cb5e92ea5dbf277e2663bfbc9b293d25bac802d74ce9409d96b",
+                "sha256:91f8479870a463fcd7219fedd09268ecd04350be669c1b27e73a111a4e380c0f",
+                "sha256:9d80f80c7dafd551ac38fe70f7fb0369e43ac9926bc92113422e79ab9de45f2d",
+                "sha256:b06e37816ab9afc45a1566ca4745ddfb066f8a65692230e0eabbf071d7b3a0f5",
+                "sha256:b2f6716f44bfaaea97783ec1738ad1ae6a5bdead10fe8f79744961caafc8ee9a",
+                "sha256:b3f361b461ec78a5afa294e63c8b0e18b5752ed784792ab70a7f4768963baf12",
+                "sha256:b6023d433d460c2106eb0bcf7ab07bc0037ae7bd8ac0080ff07e6b3911ea1097",
+                "sha256:b987fd7cde73d8e251ad3d3393ac232fdcd645bff809efd8c4d5ced8614544d5",
+                "sha256:bcaf12aea539e0952bfde3a93bd94542c8497dfcde633c89a866e1b9746d40b3",
+                "sha256:c62688c8189ff0b09c83bf79d1053845e6b2d3c0f5d567c3a18f109600161262",
+                "sha256:d39f2d1dd92ce48a176a6ccfc333ec52da20baacd77013fab1f7e99c7fe53f48",
+                "sha256:e893efdc3a911fe4387fb9bbc76134acb39a479237e47effea124834e97f052b",
+                "sha256:ecf429dee3cc73dc8a26cae5c0b2495c65d9361616ae4eb369cca61f1189a0bb",
+                "sha256:ed7a20865e778a17aedb2c2deeaaf21fa27a23435db4acf00fd10feb922efd8d",
+                "sha256:f349f978b84662c60e38e72ad56784088e9a4b0b1fbe3dff0b42982e694657d3",
+                "sha256:fac8d48c5afeb830f7427a3041fba0fb7a991ef05091034352b4dbca6f17f887",
+                "sha256:ffc9b1319b2a413e4d6b160aaa212f8dd219926bfe5181cb814c1c84c34df7f5"
             ],
-            "version": "==1.10.1"
+            "version": "==1.11.1"
         },
         "fill-voids": {
             "hashes": [
-                "sha256:00ae2663e3efbcccdb9acce555499552f7847cb93a36971bfe8483a8a6a7afff",
-                "sha256:0106982014c93e6466450dfd790a780f0ab0a98a71817eb1b7e2d064372dd621",
-                "sha256:5da32b012876aa005aa3de9056592753c90ce515b233d5a4b201971b9edc8819",
-                "sha256:6f5668db06b96d105a6f1ab1294ca406e996734b3f7c18f32cce905f4a3c2ebc",
-                "sha256:aa4ba27820ce60dc2a8edd5b3ccc48e8cf0bbb40e960410581e8b3d76dad3c98",
-                "sha256:b9e539c9f936f2edfbe85e13403df9ea421844bdd3ba8aec614efd55cf522cdc",
-                "sha256:c9bc21262de21271fce9c2ab8298871da44c7fcb3022a3592e2fa6ea732c8044",
-                "sha256:d4d282cb1f2048e69b623c1eef1f8a7bd0647c79ff2270eb47afacf87c58a4bd",
-                "sha256:d501c2f348b6ffdf084e8b324b15999284fdc4aae44ca038c91fc5eab9440d03"
+                "sha256:023d05596ec0b787181d6b98b908d719efa13929f93286183c8a9f2382e9cc9d",
+                "sha256:100fc27558947e48e907765b0ad79f18f3a742bc2b264e608b99488a4758847b",
+                "sha256:15edc426869b01f1c4acd0d86e5fb7655274df7f42a3fe0d362570ff8be97643",
+                "sha256:1e29bdfee2c8e7cbc5a7a4411a3fd12f52e66366759ec820875c24912deeecf9",
+                "sha256:1e552c601f242aa22d94dcbc197fd79a0b1f399a08c62e6643fc096938ac3be1",
+                "sha256:2b0dd7a4ac2a12deba24ff9a3473cb68aec91c3c26433ff3a8ccf238f1c68859",
+                "sha256:33218a08af797bcd5161d7c81e5c3c1c4a41873376c971da3f4859b3871b64bd",
+                "sha256:36187a1be097d65403d7ec23aedf1c4166396a830ac12d3fdbbb42c37d21bf91",
+                "sha256:401d48044b29434810ac151375c321cfa1046fb5a446b2a1854134db8c14a818",
+                "sha256:4f263d1022fc15fe500b0e9fb55dc5715388c7c4b144bfde45d10f3d84c4db89",
+                "sha256:5da10ad65381a55662878e556ba8f2eef461611bc2eb77b8f7a1398f176ea367",
+                "sha256:65ba51f9636ab86ee77f19756323b499383b936e2cbb4b3c5eb71f3d893c070d",
+                "sha256:6bc275cd26208f27d43f1eaea28dab14e87d89710d675a49a24d2262076e87ad",
+                "sha256:7ecb363a301b8f52f0727ed48f6161f5833d6058cad7994f3a7cfe6d08ad62f8",
+                "sha256:869dc6476b92dc608d6235088c36319cfd4be2633728afe97b1bdfbf6498f878",
+                "sha256:92dac48a735437c7d3631b82cd2b81a53c18a7c617eb0c1f8b0c46a6f37f7e7f",
+                "sha256:a4f17b5ef5e97c52fa5cd13f2e5588d55a37f6a887c747fac9995d7b2ff8903c",
+                "sha256:a92f83aaf894995b05e90f0279cb7b8e2083abe3143908bff9cb2a5419de2916",
+                "sha256:b398ff02191063dd45b71b57658d6a8d8643258515a782daad29cf8de1290b9a",
+                "sha256:ba04a1e63041e5dcd5b9055772d6e28054325288c658938597eabea153f0fb13",
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+                "sha256:d15e8270f5b578c027dc6d2c75f0273917f6b2ecbf703f66d2601d3ecbc7aeb1",
+                "sha256:d2390cf76983147d7e4dd471dfb4666463c74d17ab0854e1991dd95de202388b",
+                "sha256:dea95f11d8a9b092fede1e264414c3efd360e78471c26cd83da997994790dca7",
+                "sha256:dfb01ce6af548d8b8427bbd4f1aeadf3858fdc0a245154a49890c7c4e65a9d69",
+                "sha256:f1b9426b90e5d5e9cc4dbb3dae1f7031abca62001f9d12daf7163b5c695c739b",
+                "sha256:f56070dfd1b57042b04ad7229457affcce5b7c566a2967c72be8b0dfe5c6dd9a",
+                "sha256:f57f651f673d2500aa7f0d86bbbcab56113a7afccd10b351ac1a55d9990f797d",
+                "sha256:fad9df6460cd63fa8855ff5e9b2510eb16d209332722190cc1ecbc295699c6c5"
             ],
             "index": "pypi",
-            "version": "==1.1.0"
-        },
-        "future": {
-            "hashes": [
-                "sha256:b1bead90b70cf6ec3f0710ae53a525360fa360d306a86583adc6bf83a4db537d"
-            ],
-            "version": "==0.18.2"
+            "version": "==2.0.1"
         },
         "imageio": {
             "hashes": [
-                "sha256:1e4ab29b3775bb093c7a35854a0412857145450183344678829b30e72263b001",
-                "sha256:fb5fd6d3d17126bbaac9af29fe340e2c97a196eb9416d4f28c0e543744a152cf"
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             ],
-            "version": "==2.8.0"
+            "version": "==2.9.0"
         },
         "kiwisolver": {
             "hashes": [
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+                "sha256:c8fd0f1ae9d92b42854b2979024d7597685ce4ada367172ed7c09edf2cef9cb8",
+                "sha256:ca3820eb7f7faf7f0aa88de0e54681bddcb46e485beb844fcecbcd1c8bd01689",
+                "sha256:cf8b574c7b9aa060c62116d4181f3a1a4e821b2ec5cbfe3775809474113748d4",
+                "sha256:d3155d828dec1d43283bd24d3d3e0d9c7c350cdfcc0bd06c0ad1209c1bbc36d0",
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+                "sha256:fd34fbbfbc40628200730bc1febe30631347103fc8d3d4fa012c21ab9c11eca9"
             ],
-            "version": "==1.2.0"
+            "markers": "python_version >= '3.6'",
+            "version": "==1.3.1"
         },
         "matplotlib": {
             "hashes": [
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+                "sha256:1e850163579a8936eede29fad41e202b25923a0a8d5ffd08ce50fc0a97dcdc93",
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+            "markers": "python_version >= '3.6'",
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-            "version": "==1.4.2"
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             "version": "==2.4.7"
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@@ -201,6 +274,7 @@
                 "sha256:73ebfe9dbf22e832286dafa60473e4cd239f8592f699aa5adaf10050e6e1823c",
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             "version": "==2.8.1"
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@@ -208,14 +282,22 @@
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@@ -223,138 +305,176 @@
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+                "sha256:2252bc63fcccb27785726dd9d0d9a97432657a5d139390bf93cd6bdf227a4401",
+                "sha256:24b505dbcf3cb8da49d4b1447543c1021b699c84fc3701523101b62ee4adf097",
+                "sha256:421bda7131f3c0eae2260f10174ac3c49e54183b33acb927b4b572f4cd90066d",
+                "sha256:63052147c776d9f93385410c1d5a791386eb0cb5e1b93c7feac686f8dbe6eb06",
+                "sha256:874714f30822d4c1160071dac004d48ae641bfdccccbb497098c86f6589ec0f1",
+                "sha256:8791da742c24344646a4ac36adee9327491f7fff7607dffe352402b5bf25ea21",
+                "sha256:8fce78a59959f4bb4780a78c2277d617e44da7bc270bc449ff403187f6b587fc",
+                "sha256:b03275f351feffaf7450d234ffb57cce26ff5e696d01ef5f543de205f18849a9",
+                "sha256:b9f71f62725776495071b875494af86615f225b1a40902f5df452da5cfde0510",
+                "sha256:d85d405e8cf694c1f85da7f0496ea69dd4f8d8dafbdad1e29bcdc4c621fc5cf0",
+                "sha256:d90750ae76a0cad8ffb6b509b30412dcd102d27d5f34f7184b289b6687de580e",
+                "sha256:fa302f6e8fe33a8d5c6649e659655c0427eee662fe22ce69eb56fa402b520c26"
             ],
             "index": "pypi",
-            "version": "==0.6.0"
+            "version": "==0.9.0"
         },
         "tqdm": {
             "hashes": [
-                "sha256:4733c4a10d0f2a4d098d801464bdaf5240c7dadd2a7fde4ee93b0a0efd9fb25e",
-                "sha256:acdafb20f51637ca3954150d0405ff1a7edde0ff19e38fb99a80a66210d2a28f"
+                "sha256:9fdf349068d047d4cfbe24862c425883af1db29bcddf4b0eeb2524f6fbdb23c7",
+                "sha256:d666ae29164da3e517fcf125e41d4fe96e5bb375cd87ff9763f6b38b5592fe33"
+            ],
+            "index": "pypi",
+            "version": "==4.59.0"
+        },
+        "typing-extensions": {
+            "hashes": [
+                "sha256:7cb407020f00f7bfc3cb3e7881628838e69d8f3fcab2f64742a5e76b2f841918",
+                "sha256:99d4073b617d30288f569d3f13d2bd7548c3a7e4c8de87db09a9d29bb3a4a60c",
+                "sha256:dafc7639cde7f1b6e1acc0f457842a83e722ccca8eef5270af2d74792619a89f"
+            ],
+            "version": "==3.7.4.3"
+        },
+        "vtk": {
+            "hashes": [
+                "sha256:11c7b05e2a35ff5724b5c88a90ffaa1de84347ddc8293c1b74158c721db31573",
+                "sha256:3b0873e79365268b3b7d2acb238d2333f73209c27f54b76dc0cdd85483afa82e",
+                "sha256:4092bf85e1d02a76ea878bb9b822eb57ec7b73c8826e3ec29f1c8cbddcb312e3",
+                "sha256:424d7e6f8a216b7af82c672ebae82bc6e268582f802c7b32684ae9a90ef4c19e",
+                "sha256:b9d3827bf84ca11a669cf798d2a205d4ca19ed66e15e93ab2ab747f9dbe9edbe",
+                "sha256:bd4a066dbb4fb35d329278538976ad4440aeb0e5c839cc12310567238211971c",
+                "sha256:bd6efb3d3a6ead36e42ab938806364fa7dc858228a1c81e321556e43ff899d89",
+                "sha256:cbf17bc5ab7ccbda79458cf241cad8bc790a228dfa15066c50648bfb5d7abeb1",
+                "sha256:cdc7d6d556cf520e6c3c76299125a71d7be5895744ba949e2560d9332e607b50",
+                "sha256:e2ce620b2691ee8770bd9bfc0ae2ca99c0263ded20559c389a10b9b78e7750b2",
+                "sha256:e6bfc40f4cdc02f8b869b0c2023b73c6aad86fdb39fcb1823b1f4f5efd1a0001"
             ],
             "index": "pypi",
-            "version": "==4.46.0"
+            "version": "==9.0.1"
         },
         "zipfile36": {
             "hashes": [
diff --git a/run.py b/run.py
index d866b32..ec8ecfc 100644
--- a/run.py
+++ b/run.py
@@ -11,10 +11,14 @@
 from utils import getImageSeriesId, readImage, dicom2nrrd
 
 
-def do_prediction(input_image, force_cpu):
+def do_prediction(input_image, force_cpu, lobes):
     # Run segmentation
     print("Running segmentation...")
-    segmentation = mask.apply(input_image, force_cpu=force_cpu)
+    if lobes:
+        print("setting lobe model")
+        model = mask.get_model("unet", "LTRCLobes")
+
+    segmentation = mask.apply(input_image, model, force_cpu=force_cpu)
     # free memory
     torch.cuda.empty_cache()
 
@@ -93,18 +97,6 @@ def maskToCSV(mask, image, tresholds, folder_path):
 
     sitk.WriteImage(out_img, os.path.join(folder_path, "lung_mask_palette.nrrd"))
 
-    # Write lungs extraction
-    print("Writing lungs extraction...")
-    image_arr = sitk.GetArrayFromImage(image)
-    mask[mask == 2] = 1
-    lungs_arr = image_arr * mask
-    lungs_arr[mask == 0] = -1000
-    lungs = sitk.GetImageFromArray(lungs_arr)
-    lungs.SetSpacing(spacing)
-    lungs.SetDirection(direction)
-    lungs.SetOrigin(origin)
-    sitk.WriteImage(lungs, os.path.join(folder_path, "lungs.nrrd"))
-
     return perfusion_mask
 
 
@@ -182,17 +174,26 @@ def register(image_fixed, image_move, folder_out):
         default=[-940, -860, -740],
     )
 
+    parser.add_argument(
+        "--lobes", action="store_true", default=False, help="Use lobes model"
+    )
+
+    parser.add_argument(
+        "--mask2csv", action="store_true", default=False, help="compute csv"
+    )
+
     args = parser.parse_args()
 
     if args.dicomdir:
         path_image = args.dicomdir
 
         # create temp folder
-        temp_path = os.path.join(os.getcwd(), "temp/")
-        os.makedirs(temp_path, exist_ok=True)
+        # temp_path = os.path.join(os.getcwd(), "temp/")
+        # os.makedirs(temp_path, exist_ok=True)
 
-        # create path to temp files
-        nrrd_image_path = os.path.join(temp_path, "image.nrrd")
+        # # create path to temp files
+        # nrrd_image_path = os.path.join(temp_path, "image.nrrd")
+        nrrd_image_path = "./image.nrrd"
 
         # convert input dicom to nrrd
         image = dicom2nrrd(path_image, nrrd_image_path)
@@ -206,9 +207,22 @@ def register(image_fixed, image_move, folder_out):
         mask = sitk.ReadImage(args.use_mask)
         segmentation_arr = sitk.GetArrayFromImage(mask)
     else:
-        segmentation_arr = do_prediction(image, args.force_cpu)
+        segmentation_arr = do_prediction(image, args.force_cpu, args.lobes)
 
     # extract only values inside the target palette
-    maskToCSV(segmentation_arr, image, args.thresholds, args.outfolder)
+    if args.mask2csv:
+        maskToCSV(segmentation_arr, image, args.thresholds, args.outfolder)
+
+    # Write lungs extraction
+    print("Writing lungs extraction...")
+    image_arr = sitk.GetArrayFromImage(image)
+    segmentation_arr[segmentation_arr > 1] = 1
+    lungs_arr = image_arr * segmentation_arr
+    lungs_arr[segmentation_arr == 0] = -1500
+    lungs = sitk.GetImageFromArray(lungs_arr)
+    lungs.SetSpacing(image.GetSpacing())
+    lungs.SetDirection(image.GetDirection())
+    lungs.SetOrigin(image.GetOrigin())
+    sitk.WriteImage(lungs, "./lungs.nrrd")
 
     print("DONE, output in:", args.outfolder)

From c092dc7c008f6e281345cf7373e76ff20d9434b2 Mon Sep 17 00:00:00 2001
From: mattia-wsl <mat.ronzoni@gmail.com>
Date: Fri, 9 Apr 2021 11:24:27 +0200
Subject: [PATCH 2/2] add sirways rec files

---
 airw.py     | 135 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 divide.py   | 126 ++++++++++++++++++++++++++++++++++++++++++++++++
 fillMask.py |  87 +++++++++++++++++++++++++++++++++
 trees.py    |  95 ++++++++++++++++++++++++++++++++++++
 4 files changed, 443 insertions(+)
 create mode 100644 airw.py
 create mode 100644 divide.py
 create mode 100644 fillMask.py
 create mode 100644 trees.py

diff --git a/airw.py b/airw.py
new file mode 100644
index 0000000..8cc8336
--- /dev/null
+++ b/airw.py
@@ -0,0 +1,135 @@
+# segment vascular tree and airways
+
+import SimpleITK as sitk
+import os
+import numpy as np
+import argparse
+import vtk
+
+# import itk
+
+
+def connected_threshold(nrrd_image, seeds, tresh):
+    print("reading image")
+    segmentationfilter = sitk.ConnectedThresholdImageFilter()
+    segmentationfilter.SetUpper(tresh[0])
+    segmentationfilter.SetLower(tresh[1])
+    segmentationfilter.SetReplaceValue(1)
+    for i in range(0, len(seeds), 3):
+        seed = seeds[i : i + 3]
+        print(seed)
+        segmentationfilter.AddSeed(seed)
+    print("run filter")
+    result = segmentationfilter.Execute(nrrd_image)
+    return result
+
+
+if __name__ == "__main__":
+    # init arg parser
+
+    parser = argparse.ArgumentParser(description="")
+
+    parser.add_argument(
+        "--image",
+        action="store",
+        default=None,
+        help="the image",
+    )
+
+    parser.add_argument(
+        "--mask",
+        action="store",
+        default=None,
+        help="the mask",
+    )
+
+    parser.add_argument(
+        "--seeds",
+        action="store",
+        nargs="+",
+        type=int,
+        help="array of xyz",
+        default=[],
+    )
+
+    parser.add_argument(
+        "--thresholds",
+        action="store",
+        nargs="+",
+        type=int,
+        help="array of upper and lower tr",
+        default=[],
+    )
+
+    args = parser.parse_args()
+
+print(args.seeds)
+print(args.thresholds)
+
+img = sitk.ReadImage(args.image)
+out_img_path = "./airw.nrrd"
+# tree = connected_threshold(img, args.seeds, args.thresholds)
+
+seg = sitk.ConfidenceConnected(
+    img,
+    seedList=[args.seeds],
+    numberOfIterations=10,
+    multiplier=2.0,
+    initialNeighborhoodRadius=10,
+    replaceValue=1,
+)
+
+# initial sharpening ?
+# erosion, then conn tr ?
+
+# mask = sitk.ReadImage(args.mask)
+# mask = sitk.GetArrayFromImage(mask)
+# mask[mask > 1] = 1
+# tree_arr = sitk.GetArrayFromImage(tree)
+# tree_arr[mask == 1] = 0
+
+# outttt = sitk.GetImageFromArray(tree_arr)
+
+# thresholder = sitk.BinaryThresholdImageFilter()
+# thresholder.SetLowerThreshold(0)
+# # thresholder.SetUpperThreshold()
+# thresholder.SetInsideValue(1)
+# thresholder.SetOutsideValue(0)
+# tree_mask = thresholder.Execute(img)
+
+# # closing
+# closing = sitk.BinaryMorphologicalClosingImageFilter()
+# closing.SetKernelRadius(1)
+# closing.SetForegroundValue(1)
+# closed = closing.Execute(seg)
+
+# # opening
+opening = sitk.BinaryMorphologicalOpeningImageFilter()
+opening.SetKernelRadius(2)
+opening.SetForegroundValue(1)
+opened = opening.Execute(seg)
+
+# # closing
+closing = sitk.BinaryMorphologicalClosingImageFilter()
+closing.SetKernelRadius(1)
+closing.SetForegroundValue(1)
+closed = closing.Execute(opened)
+
+# smooth
+# smoother = sitk.MedianImageFilter()
+# smoother.SetRadius([0, 0, 1])
+# smooth = smoother.Execute(opened)
+
+# gaussian = sitk.SmoothingRecursiveGaussianImageFilter()
+# gaussian.SetSigma(1)
+# smooth = gaussian.Execute(opened)
+
+# surface extraction
+
+
+out = closed
+out.SetSpacing(img.GetSpacing())
+out.SetDirection(img.GetDirection())
+out.SetOrigin(img.GetOrigin())
+print("writing image")
+sitk.WriteImage(out, out_img_path, True)
diff --git a/divide.py b/divide.py
new file mode 100644
index 0000000..3a9fe29
--- /dev/null
+++ b/divide.py
@@ -0,0 +1,126 @@
+import SimpleITK as sitk
+import numpy as np
+import argparse
+import os
+
+# divide image with same serie id but different acquisition number in the same folder
+# write img_1 and img_2 in output
+
+if __name__ == "__main__":
+    # init arg parser
+
+    parser = argparse.ArgumentParser(
+        description=""
+    )
+
+    parser.add_argument(
+        "--image",
+        action="store",
+        default=None,
+        help="the target image",
+    )
+
+    parser.add_argument(
+        "--dicomdir",
+        action="store",
+        default=None,
+        help="the dicom folder of target image",
+    )
+
+    parser.add_argument(
+        "--mask",
+        action="store",
+        default=None,
+        help="the mask image",
+    )
+
+    args = parser.parse_args()
+
+    # image = sitk.ReadImage(args.image)
+    # mask = sitk.ReadImage(args.mask)
+
+    # crop = sitk.CropImageFilter()
+    # crop.SetUpperBoundaryCropSize([0,0,23])
+    # crop.SetLowerBoundaryCropSize([0,0,0])
+    # res = crop.Execute(image)
+
+
+    # res = image[:, :, 200:423]
+
+    # sitk.WriteImage(res, "./res.nrrd", True)
+
+    # img = sitk.GetImageFromArray(image)
+    # msk = sitk.GetImageFromArray(mask)
+    
+    # print('done conversion')
+
+    # out_img = img[msk]
+
+    # print(out_img)
+
+    def getImageSeriesId(file_name, series_list, desc_list):
+        print("Reading image...")
+        # A file name that belongs to the series we want to read
+
+        # Read the file's meta-information without reading bulk pixel data
+        # print('Reading image...')
+        file_reader = sitk.ImageFileReader()
+        file_reader.SetFileName(file_name)
+
+        try:
+            file_reader.ReadImageInformation()
+        except:
+            print("ERROR while reading: ", file_name)
+            print("SKIP file")
+            return
+
+        # Get the sorted file names, opens all files in the directory and reads the meta-information
+        # without reading the bulk pixel data
+        series_ID = file_reader.GetMetaData("0020|000e")
+        description = file_reader.GetMetaData("0008|103e")
+        # print('seriesId', series_ID, '\t\t descr', description)
+
+        if series_ID not in series_list:
+            series_list.append(series_ID)
+            desc_list.append(description)
+
+        return series_ID
+
+    def divideByAcqNumber(file_names):
+        seriesObject = [[],[]]
+
+        for file_name in file_names:
+            file_reader = sitk.ImageFileReader()
+            file_reader.SetFileName(file_name)
+
+            try:
+                file_reader.ReadImageInformation()
+            except:
+                print("ERROR while reading: ", file_name)
+                print("SKIP file")
+                return
+
+            acqNumber = file_reader.GetMetaData("0020|0012")
+            print(acqNumber, file_name)
+            seriesObject[int(acqNumber)-1].append(file_name)
+
+        return seriesObject
+
+    path_image = args.dicomdir
+
+    for (root, dirs, files) in os.walk(path_image):
+        series_id = getImageSeriesId(os.path.join(root, files[0]), [], [])
+
+        sorted_file_names = sitk.ImageSeriesReader.GetGDCMSeriesFileNames(
+            path_image, series_id
+        )
+
+    obj = divideByAcqNumber(sorted_file_names)
+
+    image1 = sitk.ReadImage(obj[0])
+    image2 = sitk.ReadImage(obj[1])
+
+    sitk.WriteImage(image1, "./image1.nrrd", True)
+    sitk.WriteImage(image2, "./image2.nrrd", True)
+
+
diff --git a/fillMask.py b/fillMask.py
new file mode 100644
index 0000000..0e046b8
--- /dev/null
+++ b/fillMask.py
@@ -0,0 +1,87 @@
+import SimpleITK as sitk
+import numpy as np
+import argparse
+
+if __name__ == "__main__":
+    # init arg parser
+
+    parser = argparse.ArgumentParser(description="")
+
+    parser.add_argument(
+        "--mask",
+        action="store",
+        default=None,
+        help="the binary lung mask",
+    )
+
+    parser.add_argument(
+        "--image",
+        action="store",
+        default=None,
+        help="the original image",
+    )
+
+    args = parser.parse_args()
+
+    mask = sitk.ReadImage(args.mask)
+    mask_arr = sitk.GetArrayFromImage(mask)
+
+    out_mask = mask
+
+    dims = mask.GetSize()
+
+    def fill_line(k, j, arr_in, arr_out):
+        fill = False
+        for i in range(arr_in.shape[2]):
+            px0 = arr_in[k, j, i]
+            px1 = arr_in[k, j, i + 1]
+            if px0 == 1 and px1 == 0:
+                fill = True
+            if fill:
+                arr_out[k, j, i] = 3
+            if fill and px0 == 0 and px1 == 2:  # right lung value is 2
+                break
+
+    llv = 1
+    rlv = 2
+
+    out_arr = np.copy(mask_arr)
+
+    for k in range(mask_arr.shape[0]):
+        for j in range(mask_arr.shape[1]):
+            row = mask_arr[k, j, :]
+            if (llv in row) and (rlv in row):
+                print(k, j)
+                fill_line(k, j, mask_arr, out_arr)
+
+    # binarize!
+    out_arr[out_arr == 2] = 1
+    out_arr[out_arr == 3] = 1
+
+    spacing = mask.GetSpacing()
+    direction = mask.GetDirection()
+    origin = mask.GetOrigin()
+
+    out_mask = sitk.GetImageFromArray(out_arr)
+    out_mask.SetSpacing(spacing)
+    out_mask.SetDirection(direction)
+    out_mask.SetOrigin(origin)
+
+    # close little holes
+    filler = sitk.BinaryFillholeImageFilter()
+    filler.SetForegroundValue(1)
+    filled_mask = filler.Execute(out_mask)
+
+    sitk.WriteImage(filled_mask, "./filled_mask.nrrd")
+
+    if args.image:
+        img = sitk.ReadImage(args.image)
+        img_arr = sitk.GetArrayFromImage(img)
+        o_arr = np.zeros(img_arr.shape)
+        o_arr = o_arr - 1500  # background value
+        o_arr[out_arr == 1] = img_arr[out_arr == 1]
+        out_img = sitk.GetImageFromArray(o_arr)
+        out_img.SetDirection(img.GetDirection())
+        out_img.SetSpacing(img.GetSpacing())
+        out_img.SetOrigin(img.GetOrigin())
+        sitk.WriteImage(out_img, "./filled_lungs.nrrd")
diff --git a/trees.py b/trees.py
new file mode 100644
index 0000000..980ed57
--- /dev/null
+++ b/trees.py
@@ -0,0 +1,95 @@
+# segment vascular tree and airways
+
+import SimpleITK as sitk
+import os
+import numpy as np
+import argparsew
+import vtk
+
+# import itk
+
+
+def connected_threshold(nrrd_image, seeds, tresh):
+    print("reading image")
+    segmentationfilter = sitk.ConnectedThresholdImageFilter()
+    segmentationfilter.SetUpper(tresh[0])
+    segmentationfilter.SetLower(tresh[1])
+    segmentationfilter.SetReplaceValue(1)
+    for i in range(0, len(seeds), 3):
+        seed = seeds[i : i + 3]
+        print(seed)
+        segmentationfilter.AddSeed(seed)
+    print("run filter")
+    result = segmentationfilter.Execute(nrrd_image)
+    return result
+
+
+if __name__ == "__main__":
+    # init arg parser
+
+    parser = argparse.ArgumentParser(description="")
+
+    parser.add_argument(
+        "--image",
+        action="store",
+        default=None,
+        help="the image",
+    )
+
+    parser.add_argument(
+        "--seeds",
+        action="store",
+        nargs="+",
+        type=int,
+        help="array of xyz",
+        default=[],
+    )
+
+    parser.add_argument(
+        "--thresholds",
+        action="store",
+        nargs="+",
+        type=int,
+        help="array of upper and lower tr",
+        default=[],
+    )
+
+    args = parser.parse_args()
+
+print(args.seeds)
+print(args.thresholds)
+
+img = sitk.ReadImage(args.image)
+out_img_path = "./tree.nrrd"
+# tree = connected_threshold(img, args.seeds, args.thresholds)
+
+thresholder = sitk.BinaryThresholdImageFilter()
+thresholder.SetLowerThreshold(0)
+# thresholder.SetUpperThreshold()
+thresholder.SetInsideValue(1)
+thresholder.SetOutsideValue(0)
+tree_mask = thresholder.Execute(img)
+
+# closing
+opening = sitk.BinaryMorphologicalClosingImageFilter()
+opening.SetKernelRadius(1)
+opening.SetForegroundValue(1)
+opened = opening.Execute(tree_mask)
+
+# smooth
+# smoother = sitk.MedianImageFilter()
+# smoother.SetRadius([0, 0, 1])
+# smooth = smoother.Execute(opened)
+gaussian = sitk.SmoothingRecursiveGaussianImageFilter()
+gaussian.SetSigma(1)
+smooth = gaussian.Execute(opened)
+
+# surface extraction
+
+
+out = smooth
+out.SetSpacing(img.GetSpacing())
+out.SetDirection(img.GetDirection())
+out.SetOrigin(img.GetOrigin())
+print("writing image")
+sitk.WriteImage(out, out_img_path, True)