直觀、使用簡單的界面協助使用者從不同角度分析數千個圖像數據，生成多種圖形類型可視化結果。
此外，機器學習和深度學習功能大幅提高目標識別能力，非常適合複雜性和高難度的分析，例如分析來自 3D 培養和活細胞成像的數據。
CellPathfinder 軟體是 HCA 的強大工具。
下載試用軟體。Software download
CellPathfinder 專用軟體克服挑戰
用於篩選 screening
CellPathfinder 解決篩選時遇到的瓶頸。
- 檢查多個樣品的專用界面使圖像比較變得容易，提高效率。
- 初學者也可以通過簡單的操作使用 AI 進行高級分析。
- 各種圖表創建功能和簡單的圖像和影像創建，有效減輕報告的負擔。
用於癌症研究和再生醫學篩選
CellPathfinder以專業分析技術提供領先的 HCA。
- 使用Yokogawa專有的圖像生成技術“CE Bright Field”可以對您不想染色的樣品進行無標籤分析。
- 新開發的機器學習和深度學習功能，使用簡單，難以檢測的顯像變得容易。
- 高速、高精度的罕見事件（CTC等）檢測。
應用
Details
從圖像到分析和圖表的簡單工作流程
1. 顯示圖像數據
・輕鬆比較培養孔之間的圖像
2. 加載並執行分析協議
・容易理解的圖形圖標
・可為您的分析選擇預設模板
3. Gating 圈選策略
・透過gating圈選識別對象的特徵值數據可提取特定目標
・可進一步分析提取的特定目標
4. 製作圖表
・各種圖表選項可視化結果
・圖表和圖像之間的連結可快速查看圖像
5. 檢查更多細節……列出感興趣細胞的摘要
・點滑鼠一下就能收集圖像和數值數據
基本分析功能
3D 分析
・三維空間中 Z-stack圖像的分析。 可以量化3D空間中物體的體積和位定。
圖像拚接
透過圖像拚接和分析產生平鋪圖像，可準確的量化。
非常適合跨領域的分析，例如球體、組織切片和神經突觸。
NEW!
支援向下採樣 downsampling
當不需要空間分辨率時，可以進行快速分析。
處理巨大的平鋪圖像變得比以往更容易。
手動區域定義
自動圖像處理難以識別的複雜趨勢，可以手動分析區域。
定義區域的形態可以可視化，便於分析。
Data provided by Dr. Yasuhito Shimada, Mie University Graduate School of Medicine
方便的繪圖工具
分析結果可以通過條形圖、折線圖、餅圖、散點圖、熱圖和直方圖顯示。
此外，還可以計算分析結果的EC50、IC50和Z'-Factor。
多種可選功能可實現多種分析
Basic Pack 包括從螢光圖像中收集有關細胞形態和亮度的各種定量數據所需的基本功能。
此外，通過選配功能，可以進行各種強大分析。
對比度增強明場
使用橫河電機Yokogawa的“CE Bright Field”專有圖像創建技術，可以從明場圖像中輸出兩種類型的圖像。
這是一個強大的預處理功能，用於使用明場圖像的深度學習功能進行分析。
Phase-type: 例如通過相差顯微鏡拍攝的圖像。
它可用於細胞輪廓的高精度識別和細胞表型分析。
Fluor-type: 類似螢光的圖像，可用於識別細胞核等。
機器學習
機器學習功能可在透過外觀評估的實驗中進行無偏見的數位化。
此外，只需滑鼠單點您希望軟體學習的形狀，即可執行自動形狀識別。
同一區塊的時間推移
心肌細胞和神經元活動的快速鈣振盪可以通過在整個延時過程中測量同一區域的強度來表示為波形。
對象追蹤
可追蹤單個細胞來監控動態細胞行為。
也能在細胞分裂後追蹤子細胞，從而分析細胞譜系。
分類 (Gate)
細胞可以分為具有相似特徵的細胞組。
此功能可以評估每個細胞組中的細胞數量和細胞比例，以及每個特定細胞組中的特徵量。
Cell Recognition (Deep Area Finder)
You can recognize targeted objects, such as cells and intracellular organelles by painting them using not only fluorescence images but also bright field images.
This function is useful when the analysis accuracy with conventional analysis methods are not enough.
Original image
Recognition result
Cell Counts (Deep Cell Detector)
This function detects cells with simple operation of enclosing cells.
No expertise is required.
It is possible to count cells in high-density on bright field images as well as flourescence images.
Original image
Recognition result
Cell Classification (Deep Image Gate)
You can classify phenotypes that are difficult to quantify but appear to be "something different".
Simple operation of selecting the cell groups to be classified.
No need to select effective features or set thresholds.
Classification of cell cycle (G1, Early S, SG2M) using the Fucci system
- Added 0–6.8μM etoposide to HeLa cells with Fucci
- 48-hours time lapse at 1-hour intervals at 10x; 488nm and 561nm
Control
6.8uM Etoposide
Ratio of cells in each cell cycle at each well.
EC50/IC50 Calculation (Deep Image Response)
This function enables comprehensive quantification of complex phenotypes using whole images.
Simple operation of slecting negative and positive wells and entering compound concentration information.
Any protocol to segment cells is not necessary.
提供最適合您需求的整體解決方案
以機械手臂運輸，使用 CellVoyager CV8000 或 CQ1 進行採集，用CellLibrarian 進行數據管理，用 CellPathfinder 進行圖像分析。
我們提供與使用者需求和預算相匹配的最佳組合。
查看大圖: Click
相關產品
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※CV1000不支援資料收集。
※CellPathfinder 系統包含專用軟體及工作站。
系統架構
・Software
・Workstation
・Displays
工作站規格
Model: Dell Precision
CPU: Intel® Xeon
Memory:128 GB
HDD: System（C:） 4TB Storage, （D:） 4TB
OS: Windows® Microsoft Windows10 IoT Enterprise
GPU: System（C:） Quadro K620 or Quadro P620 (High-performance GPU is not selected.), Quadro RTX5000 (High-performance GPU is selected.)
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參考
- Colony Formation
- Scratch Wound
- Cytotoxicity
- Neurite Outgrowth
- Co-culture Analysis
- Cell Tracking
Cell stage categorized using FucciTime lapse imaging of Fucci-added Hela cells was conducted over 48 hrs at 1 hr intervals. Gating was performed based on the mean intensities of 488 nm and 561 nm for each cell. They were categorized into four stages, and the cell count for each was calculated.
We have been developing a prototype of a genomic drug test support system using our CSU confocal scanner. This system administers chemical compounds that serve as potential drug candidates into living cells, which are the most basic components of all living organisms, records the changes in the amount and localization of target molecules inside cells with the CSU confocal scanner and a highly sensitive CCD camera, and processes and quantifies the captured high-resolution image data.
In this tutorial, we will learn how to perform cell tracking with CellPathfinder through the analysis of test images.
In this tutorial, a method for analyzing ramified structure, using CellPathfinder, for the analysis of the vascular endothelial cell angiogenesis function will be explained.
In this tutorial, we will learn how to perform time-lapse analysis of objects with little movement using CellPathfinder, through calcium imaging of iPS cell-derived cardiomyocytes.
In this tutorial, we will observe the change in number and length of neurites due to nerve growth factor (NGF) stimulation in PC12 cells.
In this tutorial, image analysis of collapsing stress fibers will be performed, and concentration-dependence curves will be drawn for quantitative evaluation.
In this tutorial, we will identify the cell cycles G1-phase, G2/M-phase, etc. using the intranuclear DNA content.
In this tutorial, spheroid diameter and cell (nuclei) count within the spheroid will be analyzed.
In this tutorial, a method for analyzing ramified structure, using CellPathfinder, for the analysis of the vascular endothelial cell angiogenesis function will be explained.
In this tutorial, using images of zebrafish whose blood vessels are labeled with EGFP, tiling of the images and recognition of blood vessels within an arbitrary region will be explained.
In this tutorial, intranuclear and intracytoplasmic NFκB will be measured and their ratios calculated, and a dose-response curve will be created.
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