From 3240451eca74e10b7fb59f37c56d9d88e2959ade Mon Sep 17 00:00:00 2001 From: DouSam <28208639+DouSam@users.noreply.github.com> Date: Tue, 21 Jul 2026 06:28:58 +0000 Subject: [PATCH] Update translations from SlicerLanguageTranslations Processed tutorials: STC-GEN-101_WelcomeTutorial, STC-GEN-102_FourMinuteTutorial, STC-SEG-103_AIBasedSegmentationIn3DSlicer, STC-VIS-101_VisualizationTutorial Files: STC-GEN-101_ar_SA.ts,STC-GEN-101_de.ts,STC-GEN-101_en-US.ts,STC-GEN-101_es-419.ts,STC-GEN-101_es.ts,STC-GEN-101_fr.ts,STC-GEN-101_hu.ts,STC-GEN-101_pt-BR.ts,STC-GEN-101_pt.ts,STC-GEN-101_pt_PT.ts,STC-GEN-101_ta.ts,STC-GEN-101_zh_Hans.ts,STC-GEN-101_zh_Hant.ts,STC-GEN-102_ar_SA.ts,STC-GEN-102_de.ts,STC-GEN-102_en-US.ts,STC-GEN-102_es-419.ts,STC-GEN-102_es.ts,STC-GEN-102_fr.ts,STC-GEN-102_hu.ts,STC-GEN-102_pt-BR.ts,STC-GEN-102_pt.ts,STC-GEN-102_pt_PT.ts,STC-GEN-102_ta.ts,STC-GEN-102_zh_Hans.ts,STC-GEN-102_zh_Hant.ts,STC-SEG-103_ar-SA.ts,STC-SEG-103_de.ts,STC-SEG-103_en-US.ts,STC-SEG-103_es-419.ts,STC-SEG-103_es.ts,STC-SEG-103_fr.ts,STC-SEG-103_hu.ts,STC-SEG-103_pt-BR.ts,STC-SEG-103_pt.ts,STC-SEG-103_pt_PT.ts,STC-SEG-103_ta.ts,STC-SEG-103_th.ts,STC-SEG-103_uk.ts,STC-SEG-103_zh-Hans.ts,STC-SEG-103_zh-Hant.ts,STC-VIS-101_ar_SA.ts,STC-VIS-101_de.ts,STC-VIS-101_en-US.ts,STC-VIS-101_es-419.ts,STC-VIS-101_es.ts,STC-VIS-101_fr.ts,STC-VIS-101_hu.ts,STC-VIS-101_pt-BR.ts,STC-VIS-101_pt.ts,STC-VIS-101_pt_PT.ts,STC-VIS-101_ta.ts,STC-VIS-101_zh_Hans.ts,STC-VIS-101_zh_Hant.ts --- .../Translations/text_dict_zh_Hant.json | 49 +++++++++ .../Translations/text_dict_zh_Hant.json | 39 ++++++++ .../Translations/text_dict_zh-Hant.json | 94 +++++++++--------- .../Translations/text_dict_zh_Hant.json | 99 +++++++++++++++++++ 4 files changed, 234 insertions(+), 47 deletions(-) create mode 100644 Tutorials/STC-GEN-101_WelcomeTutorial/Translations/text_dict_zh_Hant.json create mode 100644 Tutorials/STC-GEN-102_FourMinuteTutorial/Translations/text_dict_zh_Hant.json create mode 100644 Tutorials/STC-VIS-101_VisualizationTutorial/Translations/text_dict_zh_Hant.json diff --git a/Tutorials/STC-GEN-101_WelcomeTutorial/Translations/text_dict_zh_Hant.json b/Tutorials/STC-GEN-101_WelcomeTutorial/Translations/text_dict_zh_Hant.json new file mode 100644 index 00000000..635b33fc --- /dev/null +++ b/Tutorials/STC-GEN-101_WelcomeTutorial/Translations/text_dict_zh_Hant.json @@ -0,0 +1,49 @@ +{ + "0_TextBox_0": "歡迎使用 Slicer", + "0_TextBox_1": "Sonia Pujol 博士", + "0_TextBox_3": "放射學助理教授\n\n布萊根婦女醫院\n\n哈佛醫學院\n", + "1_Goal_title": "目標", + "1_Goal_body": "本教學將簡短介紹 Slicer 開放原始碼軟體的歡迎模組。", + "2_TextBox_0": "Slicer 5 基礎", + "3_TextBox_0": "Slicer 5 基礎", + "2_TextBox_1": "*Slicer 是一套開放原始碼軟體，用於醫學影像資料的分割、配準及視覺化。\n*此平台由多個獲 NIH 資助的大型聯盟，透過跨機構合作共同開發。\n*Slicer 僅供醫學研究使用，尚未獲得 FDA 核准。 ", + "3_TextBox_1": "3D Slicer 5 版本 5.10.0 包含超過 100 個模組及 190 多個擴充功能，可用於醫學影像資料的影像分割、配準及 3D 視覺化。", + "4_TextBox_0": "支援的平台", + "4_TextBox_1": "*Slicer 是一套多平台軟體，於 Mac OS X、Linux 及 Windows 平台上開發及維護。\n\n*Slicer 至少需要 2 GB RAM，以及具備 64 MB 內建顯示記憶體的獨立顯示卡。 ", + "5_WelcometoSlicer_title": "歡迎使用 Slicer", + "5_TextBox_1": "Slicer 的每個模組都包含一系列分頁，可供存取不同功能。\n\n按一下箭頭符號即可顯示各分頁的內容。 ", + "6_SlicerUserInterface_title": "Slicer 使用者介面", + "6_ArrowText_0": "工具列", + "6_TextBox_1": "3D 檢視器", + "6_TextBox_3": "Slicer 歡迎模組的使用者介面 (UI) 面板", + "6_TextBox_5": "資料探針", + "6_ArrowText_6": "2D 解剖檢視器", + "7_WelcomeModule_title": "歡迎模組", + "8_WelcomeModule_title": "歡迎模組", + "12_WelcomeModule_title": "歡迎模組", + "7_TextBox_1": "「文件與教學 (&T)」分頁包含 3D Slicer 訓練教材彙編及文件頁面的連結。", + "8_TextBox_0": "歡迎模組面板包含載入不同資料類型的捷徑，也提供一系列範例資料。\n\n按一下「下載範例資料」即可存取範例資料模組", + "9_SampleData_title": "範例資料", + "10_SampleData_title": "範例資料", + "11_SampleData_title": "範例資料", + "9_TextBox_1": "範例資料模組包含不同範例資料集的連結，可將這些資料集下載至 Slicer。", + "10_TextBox_0": "腦部 MR", + "10_TextBox_1": "胸部 CT", + "10_TextBox_2": "心臟 CT", + "10_TextBox_3": "擴散張量影像 (DTI) 資料集", + "10_TextBox_4": "腦部 MRI (腫瘤患者)", + "11_ArrowText_0": "按一下 MRHead，即可將資料集\n下載至 Slicer。", + "12_TextBox_0": "腦部 MR 掃描影像會顯示於\n2D 檢視器中。", + "13_MRBrainSampleDataset_title": "腦部 MR 範例資料集", + "14_MRBrainSampleDataset_title": "腦部 MR 範例資料集", + "15_MRBrainSampleDataset_title": "腦部 MR 範例資料集", + "13_TextBox_0": "將滑鼠移至紅色檢視器左上角的小圖釘圖示，即可顯示檢視器選單", + "14_TextBox_1": "按一下連結圖示，將三個 2D 檢視器全部連結，再按一下旁邊的眼睛圖示\n即可在 3D 檢視器中顯示切片", + "15_TextBox_0": "軸向、冠狀及矢狀切片會顯示在 3D 檢視器中。\n使用工具列中的綠色箭頭返回歡迎模組", + "16_GoingFurther_title": "延伸學習", + "17_TextBox_0": "延伸學習", + "16_TextBox_0": "如要深入瞭解 Slicer 及其各項功能，請造訪 Slicer 教材彙編", + "17_TextBox_1": "https://training.slicer.org/", + "18_TextBox_0": "致謝", + "18_TextBox_1": "國家醫學影像計算\n聯盟\nNIH U54EB005149\n\n神經影像分析中心\nNIH P41EB015902\n\nChan Zuckerberg Initiative (CZI)" +} \ No newline at end of file diff --git a/Tutorials/STC-GEN-102_FourMinuteTutorial/Translations/text_dict_zh_Hant.json b/Tutorials/STC-GEN-102_FourMinuteTutorial/Translations/text_dict_zh_Hant.json new file mode 100644 index 00000000..6ed36b0d --- /dev/null +++ b/Tutorials/STC-GEN-102_FourMinuteTutorial/Translations/text_dict_zh_Hant.json @@ -0,0 +1,39 @@ +{ + "0_TextBox_0": "Slicer 四分鐘教學\n", + "0_TextBox_1": "Sonia Pujol 博士", + "0_TextBox_3": "放射學助理教授\n布萊根婦女醫院\n哈佛醫學院", + "1_TextBox_0": "Slicer 四分鐘教學", + "16_TextBox_0": "Slicer 四分鐘教學", + "1_TextBox_1": "本教學將在 4 分鐘內簡介 Slicer 5 醫學影像分析軟體的 3D 視覺化功能。 ", + "2_TextBox_0": "Slicer 5 軟體與資料集 (&D)", + "2_TextBox_1": "*從 http://download.slicer.org 下載 Slicer 5 軟體\n\n*從 https://www.slicer.org/wiki/Documentation/4.10/Training 下載 Slicer4minute 資料集", + "3_3DSlicerversion5_title": "3D Slicer 版本 5", + "4_TextBox_0": "3D Slicer 場景", + "4_TextBox_1": "*Slicer 場景是一個 MRML (Medical Reality Modeling Language) 檔案，其中包含載入 Slicer 的元素清單 (影像體積、模型、基準點、變換等)。\n*在以下範例中，我們使用由頭部 MRI 掃描影像及 3D 模型組成的「Slicer4minute.mrml」場景。\n*場景檔案及資料集已儲存為 MRB (Medical Reality Bundle) 檔案。\n*MRB 檔案格式是 Slicer 的封存檔案格式。", + "5_LoadingtheSlicer4minutedataset_title": "載入 Slicer4minute 資料集", + "5_TextBox_1": "將 slicer4minute.mrb 拖放至 Slicer，以載入場景", + "6_Slicer4minuteScene_title": "Slicer4minute 場景", + "6_TextBox_1": "Slicer 會顯示 slicer4minute 場景中的元素。此場景包含腦部 MRI 掃描影像及 3D 表面模型。", + "7_3DVisualization_title": "3D 視覺化", + "9_3DVisualization_title": "3D 視覺化", + "10_3DVisualization_title": "3D 視覺化", + "11_3DVisualization_title": "3D 視覺化", + "13_3DVisualization_title": "3D 視覺化", + "14_3DVisualization_title": "3D 視覺化", + "15_3DVisualization_title": "3D 視覺化", + "7_TextBox_0": "選取「模型」模組", + "8_3Dvisualization_title": "3D 視覺化", + "8_TextBox_0": "按一下紅色切片左上角的圖釘圖示，以顯示切片檢視器選單。\n按一下眼睛圖示，以在 3D 檢視器中顯示軸向切片", + "9_TextBox_1": "使用紅色檢視器的滑桿瀏覽軸向 MR 切片。\n\nSlicer 會同時在 3D 檢視器中顯示軸向切片", + "10_TextBox_0": "選取 Skin 模型，並使用「3D 顯示」分頁中的「不透明度」滑桿降低其不透明度", + "10_TextBox_1": "可透過 Skin 模型看到 skull_bone.vtk 模型。", + "11_TextBox_0": "將滑鼠移至 3D 檢視器中，按住滑鼠左鍵拖曳並旋轉模型。\n按下滑鼠右鍵即可放大或縮小", + "12_AnatomicalViews_title": "解剖視圖", + "12_TextBox_0": "按一下紅色及綠色檢視器左上角的圖釘圖示，以顯示切片檢視器選單\n\n按一下眼睛圖示，以在 3D 檢視器中顯示軸向及冠狀切片", + "13_TextBox_0": "關閉顱骨的可見性，以顯示腦部白質模型", + "14_TextBox_0": "白質表面以及左、右視神經會顯示在檢視器中", + "15_TextBox_0": "選取 hemispheric_white_matter.vtk 模型\n\n在「3D 顯示」分頁中核取「裁切」\n\n在「裁切平面」分頁中選取「綠色切片裁切」選項，並核取「負側」", + "16_TextBox_1": "*本教學簡短介紹了如何在 Slicer 中以互動方式對 MRI 資料及 3D 模型進行 3D 視覺化。\n\n*Slicer 5 訓練教材彙編包含一系列教學及預先計算的資料集，可用來學習如何使用此軟體。", + "17_TextBox_0": "致謝", + "17_TextBox_1": "國家醫學影像計算\n聯盟\nNIH U54EB005149\n\n神經影像分析中心\nNIH P41EB015902\n" +} \ No newline at end of file diff --git a/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_zh-Hant.json b/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_zh-Hant.json index f7d01c66..6f6e6924 100644 --- a/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_zh-Hant.json +++ b/Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_zh-Hant.json @@ -1,49 +1,49 @@ { - "0_TextBox_0": "AI-based Segmentation in 3D Slicer", - "0_TextBox_1": "Sonia Pujol, Ph. D. \nBrigham and Women's Hospital,\nHarvard Medical School\nBoston, MA", - "0_TextBox_3": "Slicer Ribeirão Preto Workshop\nJune 30, 2025", - "1_TextBox_0": "Manual vs AI-powered Segmentation", - "2_TextBox_0": "Manual vs AI-powered Segmentation", - "1_TextBox_1": "Medical images have traditionally been manually segmented, which is a time-consuming process that requires intensive effort by radiologists and is subject to inter-reader variability.", - "2_TextBox_1": "In the past decade, image segmentation has been powered by the development of deep learning algorithms (e.g. nnUnet by the German Cancer Research Center (DKFZ)/Helmholtz Research).\n\n\nAI-powered segmentation tools can reduce the segmentation time and provide more reproducible results.", - "3_TextBox_0": "AI Terminology", - "3_TextBox_1": "A Model is an AI algorithm that was trained to perform a specific task (e.g. brain tumor segmentation model).\n\nThe Weights of an AI model are small numbers that determine how much importance the model gives to different image features.\n\nDuring the Training phase, a model learns patterns from data labelled by experts and adjusts its weights to improve its predictions.\n\nDuring the Validation/Test phase, the model is evaluated on a separate set of data not used during the Training phase.\n\nDuring Inference, the model is applied to new datasets to perform the specific task it was trained for.", - "4_TextBox_0": "3D Slicer AI Tutorial", - "4_TextBox_1": "This tutorial focuses on running inference tasks using various pre-trained AI models for automated segmentation of anatomical and pathological structures.", - "5_TextBox_0": "MONAIAuto3DSeg Slicer extension", - "6_TextBox_0": "MONAIAuto3DSeg Slicer extension", - "5_TextBox_1": "This tutorial uses the pre-trained models of the MONAIAuto3DSeg Slicer extension.\n\n\nThe tool is designed to work on laptops or on average desktop computer without a GPU.", - "6_TextBox_1": "Multiple modalities Support (CT, MRI).\n\n\nMultiple anatomies (head, thorax, abdomen, pelvis, etc.).\n\n\nMultiple pathologies (tumor, hemorrhage, edema).", - "7_TextBox_0": "Slicer AI Tutorial: Segmentation Tasks", - "7_TextBox_1": "Segmentation Task #1: Prostate \n\n\nSegmentation Task #2: Brain Glioma \n\n\nSegmentation Task #3: Whole Body Segmentation", - "8_TextBox_0": "AI Segmentation Task #1: Prostate", - "9_TextBox_1": "AI-based Segmentation of Peripheral Zone (PZ) and Transition Zone (TZ) of the prostate on T2-weighted MRI Images.\n\n\nDataset:\nmsd_prostate_01-t2\nmsd_prostate_01-adc", - "10_TextBox_1": "Click on Add Data in the Welcome to Slicer module\n\nClick on Choose Directory to Add and browse to the location of the Slicer datasets\n\nSelect the dataset3_ProstateMRI and click on Open", - "11_TextBox_0": "Slicer loads the prostate MRI dataset", - "12_TextBox_0": "Click on Welcome to Slicer in the Modules' menu and browse to the category Segmentation\n\nSelect the MONAIAuto3DSeg module", - "13_TextBox_0": "Enter the model's name Prostate in the Segmentation model menu", - "13_TextBox_1": "Select the model Prostate - Multisequence", - "14_TextBox_0": "Enter the Input T2 volume msd-prostate-01-t2 and the Input ADC volume msd-prostate-01-adc", - "14_TextBox_1": "Click on Create new segmentation on Apply", - "15_TextBox_0": "Slicer starts the inference", - "16_TextBox_0": "Slicer shows the results of the AI-based prostate segmentation", - "17_TextBox_0": "AI Segmentation Task #2: Brain Glioma", - "18_TextBox_1": "AI-based Segmentation of Neoplasm, Necrosis and Edema in Brain MRI images.\n\n\nDatasets:\n1) BraTS-GLI_00005-000-t1n (T1-weighted)\n2) BraTS-GLI_00005-000-t1c (T1-weighted post-Gd)\n3) BraTS-GLI_00005-000-t2w (T2-weighted)\n4) BraTS-GLI_00005-000-t2f (T2-FLAIR )", - "19_TextBox_1": "Click on Add Data in the Welcome to Slicer module\n\nClick on Choose File(s) to Add and browse to the location of the Slicer datasets\n\nIn the subdirectory dataset4_BrainMRI_Glioma, select the four datasets BraTS-GLI-00006-t1c.nii.gz, BraTS-GLI-00006-t1n.nii.gz, BraTS-GLI-00006-t2f.nii.gz, BraTS-GLI-00006-t2w.nii.gz\n\nClick on Open", - "20_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Brain Tumor Segmentation in the Segmentation model menu", - "20_TextBox_1": "Select the model Brain Tumor Segmentation (BRATS) GLI", - "21_TextBox_0": "Enter the input volumes as follows:\n\nInput T2F volume: BraTS-GLI_00005-000-t2f\nInput T1C volume: BraTS-GLI_00005-000-t1c\nInput T1N volume: BraTS-GLI_00005-000-t1n\nInput T2W volume: BraTS-GLI_00005-000-t2w\n\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", - "22_TextBox_1": "Slicer starts running the inference task\n\nOnce the segmentation is done, 'Processing finished' appears in the Slicer GUI", - "23_TextBox_1": "Click on Show 3D to display the 3D segments in the 3D Viewer", - "24_TextBox_0": "AI Segmentation Task #3: Whole Body Segmentation", - "25_TextBox_1": "AI-based Segmentation of the whole body.\n\n\nDataset:\nCT_ThoraxAbdomen", - "26_TextBox_0": "In the Add DICOM Data module, select the Patient patient1 and double click onthe image CT_Thorax_Abdomen to load it in Slicer", - "27_TextBox_0": "Select the module MONAIAuto3DSeg and enter the model's name Whole Body Segmentation in the Segmentation model menu", - "27_TextBox_1": "Select the model Whole Body Segmentation TS1-quick", - "28_TextBox_0": "Select the input Volume 6:CT_Thorax_Abdomen,\n\nClick on Create new Segmentation on Apply\n\nClick on Apply to start the segmentation", - "29_TextBox_0": "Slicer displays the results of the AI-based segmentation using the Whole Body Segmentation TS1-quick", - "30_TextBox_0": "Conclusion", - "30_TextBox_1": "The 3D Slicer MONAIAuto3DSeg extension provides fast AI-based segmentation of anatomical and pathological structures.\n\n\nThe module can run on standard laptop and desktop computers with no GPU.", - "31_TextBox_0": "Acknowledgements", - "31_TextBox_1": "The 3D Slicer internationalization project and the 3D Slicer for Latin America project have been made possible through funding by the Chan Zuckerberg Initiative." + "0_TextBox_0": "3D Slicer 的 AI 分割", + "0_TextBox_1": "Sonia Pujol 博士\n布萊根婦女醫院\n哈佛醫學院\n美國麻薩諸塞州波士頓", + "0_TextBox_3": "Slicer Ribeirão Preto 工作坊\n2025 年 6 月 30 日", + "1_TextBox_0": "手動分割與 AI 分割", + "2_TextBox_0": "手動分割與 AI 分割", + "1_TextBox_1": "傳統上，醫學影像採用手動分割。這項流程相當耗時，需要放射科醫師投入大量精力，且會受到判讀者間變異影響。", + "2_TextBox_1": "過去十年間，影像分割因深度學習演算法的發展而快速進步 (例如德國癌症研究中心 (DKFZ)／Helmholtz Research 開發的 nnUnet)。\n\n\nAI 分割工具可縮短分割時間，並提供更具再現性的結果。", + "3_TextBox_0": "AI 術語", + "3_TextBox_1": "模型 (Model) 是經過訓練、可執行特定工作 (例如腦腫瘤分割) 的 AI 演算法。\n\nAI 模型的權重 (Weights) 是一些較小的數值，用來決定模型賦予不同影像特徵的重要程度。\n\n在訓練 (Training) 階段，模型會從專家標註的資料中學習模式，並調整權重以改善預測結果。\n\n在驗證／測試 (Validation/Test) 階段，會使用未用於訓練的另一組資料評估模型。\n\n在推論 (Inference) 階段，模型會套用於新的資料集，以執行受訓的特定工作。", + "4_TextBox_0": "3D Slicer AI 教學", + "4_TextBox_1": "本教學著重於使用各種預先訓練的 AI 模型執行推論工作，以自動分割解剖與病理結構。", + "5_TextBox_0": "MONAIAuto3DSeg Slicer 擴充功能", + "6_TextBox_0": "MONAIAuto3DSeg Slicer 擴充功能", + "5_TextBox_1": "本教學使用 MONAIAuto3DSeg Slicer 擴充功能的預先訓練模型。\n\n\n此工具可在沒有 GPU 的筆記型電腦或一般桌上型電腦上運作。", + "6_TextBox_1": "支援多種影像模態 (CT、MRI)。\n\n\n支援多種解剖部位 (頭部、胸部、腹部、骨盆等)。\n\n\n支援多種病理狀況 (腫瘤、出血、水腫)。", + "7_TextBox_0": "Slicer AI 教學：分割工作", + "7_TextBox_1": "分割工作 #1：前列腺\n\n\n分割工作 #2：腦膠質瘤\n\n\n分割工作 #3：全身分割", + "8_TextBox_0": "AI 分割工作 #1：前列腺", + "9_TextBox_1": "在 T2 加權 MRI 影像上，以 AI 分割前列腺的周邊區 (PZ) 與移行區 (TZ)。\n\n\n資料集：\nmsd_prostate_01-t2\nmsd_prostate_01-adc", + "10_TextBox_1": "在「歡迎使用 Slicer」模組中點選「新增資料」\n\n點選「選擇要加入的目錄」，並瀏覽至 Slicer 資料集所在位置\n\n選取 dataset3_ProstateMRI，然後點選「開啟」", + "11_TextBox_0": "Slicer 載入前列腺 MRI 資料集", + "12_TextBox_0": "在模組選單中點選「歡迎使用 Slicer」，然後瀏覽至「分割」類別\n\n選取 MONAIAuto3DSeg 模組", + "13_TextBox_0": "在「分割模型」選單中輸入模型名稱 Prostate", + "13_TextBox_1": "選取 Prostate - Multisequence 模型", + "14_TextBox_0": "輸入 T2 影像體積 msd-prostate-01-t2 與 ADC 影像體積 msd-prostate-01-adc", + "14_TextBox_1": "點選「按一下套用時建立新的分割」", + "15_TextBox_0": "Slicer 開始執行推論", + "16_TextBox_0": "Slicer 顯示 AI 前列腺分割結果", + "17_TextBox_0": "AI 分割工作 #2：腦膠質瘤", + "18_TextBox_1": "在腦部 MRI 影像上，以 AI 分割腫瘤、壞死與水腫。\n\n\n資料集：\n1) BraTS-GLI_00005-000-t1n (T1 加權)\n2) BraTS-GLI_00005-000-t1c (T1 加權，注射 Gd 後)\n3) BraTS-GLI_00005-000-t2w (T2 加權)\n4) BraTS-GLI_00005-000-t2f (T2-FLAIR)", + "19_TextBox_1": "在「歡迎使用 Slicer」模組中點選「新增資料」\n\n點選「選擇要加入的檔案」，並瀏覽至 Slicer 資料集所在位置\n\n在 dataset4_BrainMRI_Glioma 子目錄中，選取四個資料集 BraTS-GLI-00006-t1c.nii.gz、BraTS-GLI-00006-t1n.nii.gz、BraTS-GLI-00006-t2f.nii.gz、BraTS-GLI-00006-t2w.nii.gz\n\n點選「開啟」", + "20_TextBox_0": "選取 MONAIAuto3DSeg 模組，並在「分割模型」選單中輸入模型名稱 Brain Tumor Segmentation", + "20_TextBox_1": "選取 Brain Tumor Segmentation (BRATS) GLI 模型", + "21_TextBox_0": "依下列方式輸入影像體積：\n\n輸入 T2F 影像體積：BraTS-GLI_00005-000-t2f\n輸入 T1C 影像體積：BraTS-GLI_00005-000-t1c\n輸入 T1N 影像體積：BraTS-GLI_00005-000-t1n\n輸入 T2W 影像體積：BraTS-GLI_00005-000-t2w\n\n\n點選「按一下套用時建立新的分割」\n\n點選「套用」以開始分割", + "22_TextBox_1": "Slicer 開始執行推論工作\n\n分割完成後，Slicer 圖形使用者介面會顯示「處理完成」", + "23_TextBox_1": "點選「顯示 3D」，以在 3D 檢視器中顯示 3D 分割區段", + "24_TextBox_0": "AI 分割工作 #3：全身分割", + "25_TextBox_1": "以 AI 分割全身。\n\n\n資料集：\nCT_ThoraxAbdomen", + "26_TextBox_0": "在「新增 DICOM 資料」模組中選取病患 patient1，然後按兩下影像 CT_Thorax_Abdomen，將其載入 Slicer", + "27_TextBox_0": "選取 MONAIAuto3DSeg 模組，並在「分割模型」選單中輸入模型名稱 Whole Body Segmentation", + "27_TextBox_1": "選取 Whole Body Segmentation TS1-quick 模型", + "28_TextBox_0": "選取輸入影像體積 6:CT_Thorax_Abdomen，\n\n點選「按一下套用時建立新的分割」\n\n點選「套用」以開始分割", + "29_TextBox_0": "Slicer 顯示使用 Whole Body Segmentation TS1-quick 進行 AI 分割的結果", + "30_TextBox_0": "結論", + "30_TextBox_1": "3D Slicer MONAIAuto3DSeg 擴充功能可快速以 AI 分割解剖與病理結構。\n\n\n此模組不需 GPU，即可在一般筆記型與桌上型電腦上執行。", + "31_TextBox_0": "致謝", + "31_TextBox_1": "3D Slicer 國際化專案與 3D Slicer 拉丁美洲專案得以實現，有賴 Chan Zuckerberg Initiative 資助。" } \ No newline at end of file diff --git a/Tutorials/STC-VIS-101_VisualizationTutorial/Translations/text_dict_zh_Hant.json b/Tutorials/STC-VIS-101_VisualizationTutorial/Translations/text_dict_zh_Hant.json new file mode 100644 index 00000000..c464d286 --- /dev/null +++ b/Tutorials/STC-VIS-101_VisualizationTutorial/Translations/text_dict_zh_Hant.json @@ -0,0 +1,99 @@ +{ + "0_TextBox_0": "3D Slicer 資料載入與 3D 視覺化基礎", + "0_TextBox_1": "作者：Sonia Pujol 博士", + "0_TextBox_2": "2024 年 11 月 24 日", + "0_TextBox_3": "布萊根婦女醫院暨哈佛醫學院放射學助理教授", + "1_TextBox_0": "整體目標", + "1_TextBox_1": "本教學介紹在 3D Slicer 中載入及檢視 DICOM 影像與 3D 模型的基本操作。", + "2_TextBox_0": "學習目標", + "2_TextBox_1": " • 完成本教學後，您將能夠\n\n• 在 Slicer 中載入 DICOM 影像並進行視覺化\n\n• 對 CT 資料進行影像體積渲染\n\n• 載入由 MRI 資料重建的 3D 模型並進行視覺化", + "3_TextBox_0": "教學教材", + "3_TextBox_1": "• 3D Slicer 5.10 版\n\n• 3D VisualizationDataSet.zip", + "4_TextBox_0": "教學資料集", + "33_TextBox_0": "教學資料集", + "4_TextBox_1": "3DVisualizationDataset.zip 檔案包含兩個資料夾：\n\n- dataset1_Thorax_Abdomen \n- dataset2_Head\n\n請在電腦上解壓縮 3DVisualizationDataset.zip 檔案以存取資料集", + "5_TextBox_0": "免責聲明", + "5_TextBox_1": "• 3D Slicer 是依 BSD 類型授權條款散布的自由開放原始碼軟體應用程式。\n\n\n• 本軟體未經 FDA 核准，也未取得 CE 標誌，僅供研究使用。\n", + "6_TextBox_0": "教學大綱", + "6_TextBox_1": "• 第 1 部分：載入與檢視 DICOM 資料\n\n• 第 2 部分：影像體積渲染\n\n\n• 第 3 部分：載入與檢視 3D 模型", + "7_TextBox_0": "第 1 部分：載入 DICOM 資料", + "8_LoadingaDICOMvolume_title": "載入 DICOM 影像體積", + "9_LoadingaDICOMvolume_title": "載入 DICOM 影像體積", + "8_TextBox_0": "Slicer 顯示 DICOM 模組的使用者介面", + "8_TextBox_1": "patient1 檢查包含一組胸腹部 CT 資料集", + "9_ArrowText_1": "選取 patient1 並點選「載入」\n即可將資料集載入 Slicer", + "10_VisualizingDICOMimages_title": "DICOM 影像視覺化", + "11_VisualizingDICOMimages_title": "DICOM 影像視覺化", + "12_VisualizingDICOMimages_title": "DICOM 影像視覺化", + "13_VisualizingDICOMimages_title": "DICOM 影像視覺化", + "14_VisualizingDICOMimages_title": "DICOM 影像視覺化", + "15_VisualizingDICOMimages_title": "DICOM 影像視覺化", + "16_VisualizingDICOMimages_title": "DICOM 影像視覺化", + "17_VisualizingDICOMimages_title": "DICOM 影像視覺化", + "18_VisualizingDICOMimages_title": "DICOM 影像視覺化", + "10_TextBox_0": "Slicer 顯示 CT 胸腹部資料集的軸向、\n冠狀及矢狀\n影像 ", + "11_ArrowText_1": "在 DICOM 上按一下滑鼠左鍵，以顯示\nSlicer 模組清單", + "11_ArrowText_2": "選取「影像體積」\n模組", + "12_ArrowText_0": "點選「腹部 CT」\n預設集，以自動調整\nCT 資料集顯示的\n窗位／窗寬", + "13_TextBox_1": "將滑鼠游標移至\n紅色檢視器中的\n紅色橫幅上，以顯示切片\n選單。\n\n\n點選「連結」圖示，以連結\n所有切片視圖的\n切片控制項。\n\n\n點選「眼睛」圖示，以\n在 3D 檢視器中顯示三個解剖\n切片", + "14_TextBox_0": "三個解剖切片\n顯示於 3D 檢視器中。", + "15_ArrowText_0": "點選 Slicer 版面配置選單\n圖示，然後選取\n「寬螢幕標準」版面配置", + "16_TextBox_0": "Slicer 將版面配置\n切換為「寬螢幕\n標準」版面配置", + "17_TextBox_0": "在 3D 檢視器中按住\n滑鼠右鍵以縮放", + "18_TextBox_0": "在 3D 檢視器中按住\n滑鼠左鍵以旋轉影像", + "19_3DViewerController_title": "3D 檢視器控制器", + "20_3DViewerController_title": "3D 檢視器控制器", + "19_TextBox_1": "將滑鼠游標移至\n3D 檢視器視窗藍色橫幅的\n圖釘圖示上，以\n顯示 3D 視圖控制器\n\n點選 3D 視圖\n控制器頂端列的\n第二個圖示，將 3D 視圖\n置中顯示場景", + "20_ArrowText_1": "從模組清單選取「影像體積渲染」\n模組 ", + "21_TextBox_0": "第 2 部分：影像體積渲染", + "22_TextBox_0": "影像體積渲染", + "23_VolumeRendering_title": "影像體積渲染", + "24_VolumeRendering_title": "影像體積渲染", + "25_VolumeRendering_title": "影像體積渲染", + "26_VolumeRendering_title": "影像體積渲染", + "27_VolumeRendering_title": "影像體積渲染", + "28_VolumeRendering_title": "影像體積渲染", + "29_VolumeRendering_title": "影像體積渲染", + "30_VolumeRendering_title": "影像體積渲染", + "31_VolumeRendering_title": "影像體積渲染", + "22_TextBox_1": "• 影像體積渲染\n技術可呈現 3D\n資料集的 3D\n視覺化效果\n\n• Slicer 的影像體積渲染\n模組可讓使用者以互動方式\n呈現 DICOM 影像的 3D\n視覺化效果", + "23_ArrowText_0": "在「顯示」分頁點選「預設」\n並選取「CT-Cardiac3」預設集 ", + "24_TextBox_2": "選取「VTK GPU 光線投射渲染」\n點選「影像體積」分頁中的眼睛圖示，以在\n3D 檢視器中顯示影像體積渲染結果", + "25_ArrowText_0": "使用位移滑桿\n變更傳遞\n函數並顯示\n主動脈", + "26_ArrowText_0": "點選「顯示 ROI」，以\n在 3D 檢視器中顯示感興趣區域\n（ROI），並\n勾選「啟用」選項", + "27_TextBox_0": "在 2D 檢視器中關閉\n軸向、矢狀及冠狀\n切片的顯示 \n\n\n使用彩色\n控制點，將 ROI 移至\n左腎周圍", + "28_ArrowText_0": "點選眼睛圖示，以\n顯示影像體積渲染\n結果", + "29_TextBox_0": "Slicer 顯示\n左腎的影像體積渲染\n結果 ", + "30_TextBox_0": "擴大 ROI，以產生\n右腎的影像體積渲染\n結果", + "31_ArrowText_1": "在主選單中點選「檔案」，\n再點選「關閉場景」", + "32_TextBox_0": "第 3 部分：載入與\n檢視 3D 模型\n", + "33_TextBox_1": "• dataset2_Head 資料夾包含名為 Head_scene.mrb 的 Slicer 場景\n\n• 此場景包含哈佛醫學院布萊根婦女醫院放射科所開發之 SPL 腦部圖譜的 3D 模型（NIH P41 RR013218、NIH R01 MH05074）", + "34_TextBox_0": "Slicer 場景", + "34_TextBox_1": "Slicer 會將所有載入的資料儲存在稱為場景的儲存區中\n\n\n每個資料集（例如影像體積、表面模型或點集）在 Slicer 場景中都以節點表示。\n\n\n所有 Slicer 模組都會處理儲存在 Slicer 場景中的資料。", + "35_LoadingaScene_title": "載入場景", + "35_TextBox_0": "Slicer 顯示頭部的 3D\n表面模型與\n2D MRI 切片", + "36_Viewing3Dmodels_title": "檢視 3D 模型", + "37_Viewing3Dmodels_title": "檢視 3D 模型", + "38_Viewing3Dmodels_title": "檢視 3D 模型", + "39_Viewing3Dmodels_title": "檢視 3D 模型", + "40_Viewing3Dmodels_title": "檢視 3D 模型", + "36_ArrowText_0": "將游標移至\n圖釘圖示上，以顯示\n切片選單，然後點選\n眼睛圖示，以在\n3D 檢視器中顯示軸向切片", + "37_ArrowText_1": "在模型清單中選取\n「模型」模組", + "38_ArrowText_0": "Slicer 顯示已載入\n場景的\n3D 模型清單\n\n選取 Skin.vtk 模型", + "39_ArrowText_0": "使用可見度滑桿\n降低 Skin 模型的\n不透明度", + "39_TextBox_1": "透過 Skin 模型可看到\n顱骨與眼球\n模型", + "40_ArrowText_1": "選取顱骨模型，\n然後點選眼睛\n圖示以關閉它的\n顯示", + "40_TextBox_2": "透過 Skin 模型可看到\n白質與視神經\n模型", + "41_Interactingwith3Dmodels_title": "與 3D 模型互動", + "42_Interactingwith3Dmodels_title": "與 3D 模型互動", + "43_Interactingwith3Dmodels_title": "與 3D 模型互動", + "44_Interactingwith3Dmodels_title": "與 3D 模型互動", + "41_ArrowText_0": "點選眼睛圖示，以\n在 3D 檢視器中顯示\n冠狀切片", + "42_ArrowText_2": "選取半球\n白質模型，並\n選取「裁切」選項", + "43_TextBox_0": "將冠狀切片\n向後移動，以顯示\n視交叉", + "44_TextBox_0": "Slicer 顯示\n視交叉的 3D 視圖", + "45_TextBox_0": "結論", + "45_TextBox_1": "• 3D Slicer 提供載入與檢視 3D 醫學影像資料的進階功能\n\n• 本教學示範如何使用影像體積渲染與 3D 表面建模，以互動方式呈現 CT 及 MRI 資料\n\n\n聯絡方式：spujol@bwh.harvard.edu", + "46_TextBox_0": "致謝", + "46_TextBox_1": "神經影像分析中心（NIBIB P41 EB015902）" +} \ No newline at end of file