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49 changes: 49 additions & 0 deletions Tutorials/STC-GEN-101_WelcomeTutorial/Translations/text_dict_zh_Hant.json
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{
"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)"
}
39 changes: 39 additions & 0 deletions Tutorials/STC-GEN-102_FourMinuteTutorial/Translations/text_dict_zh_Hant.json
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{
"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"
}
94 changes: 47 additions & 47 deletions Tutorials/STC-SEG-103_AIBasedSegmentationIn3DSlicer/Translations/text_dict_zh-Hant.json
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{
"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。"
}
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