外科手術的進步對急性和慢性疾病的管理，延長壽命和不斷擴大生存范圍都產生了重大影響。如圖1所示，這些進步得益于診斷，成像和外科器械的持續技術發展。這些技術中，深度學習對推動術前手術規劃尤其重要。手術規劃中要根據現有的醫療記錄來計劃手術程序，而成像對于手術的成功至關重要。在現有的成像方式中，X射線，CT，超聲和MRI是實際中常用的方式。基于醫學成像的常規任務包括解剖學分類，檢測，分割和配準。

圖1：概述了流行的AI技術，以及在術前規劃，術中指導和外科手術機器人學中使用的AI的關鍵要求，挑戰和子區域。

1、分類

分類輸出輸入的診斷值，該輸入是單個或一組醫學圖像或器官或病變體圖像。除了傳統的機器學習和圖像分析技術，基于深度學習的方法正在興起[1]。對于后者，用于分類的網絡架構由用于從輸入層提取信息的卷積層和用于回歸診斷值的完全連接層組成。

例如，有人提出了使用GoogleInception和ResNet架構的分類管道來細分肺癌，膀胱癌和乳腺癌的類型[2]。Chilamkurthy等證明深度學習可以識別顱內出血，顱骨骨折，中線移位和頭部CT掃描的質量效應[3]。與標準的臨床工具相比，可通過循環神經網絡（RNN）實時預測心臟外科手術后患者的死亡率，腎衰竭和術后出血[4]。ResNet-50和Darknet-19已被用于對超聲圖像中的良性或惡性病變進行分類，顯示出相似的靈敏度和更G的特異性[5]。

2、檢測

檢測通常以邊界框或界標的形式提供感興趣區域的空間定位，并且還可以包括圖像或區域J別的分類。同樣，基于深度學習的方法在檢測各種異常或醫學狀況方面也顯示出了希望。用于檢測的DCNN通常由用于特征提取的卷積層和用于確定邊界框屬性的回歸層組成。

為了從4D正電子發射斷層掃描（PET）圖像中檢測前列腺癌，對深度堆疊的卷積自動編碼器進行了訓練，以提取統計和動力學生物學特征[6]。對于肺結節的檢測，提出了具有旋轉翻譯組卷積（3D G-CNN）的3D CNN，具有良好的準確性，靈敏度和收斂速度[7]。對于乳腺病變的檢測，基于深度Q網絡擴展的深度強化學習（DRL）用于從動態對比增強MRI中學習搜索策略[8]。為了從CT掃描中檢測出急性顱內出血并改善網絡的可解釋性，Lee等人[9]使用注意力圖和迭代過程來模仿放射科醫生的工作流程。

3、分割

分割可被視為像素J或體素J圖像分類問題。由于早期作品中計算資源的限制，每個圖像或卷積都被劃分為小窗口，并且訓練了CNN來預測窗口中心位置的目標標簽。通過在密集采樣的圖像窗口上運行CNN分類器，可以實現圖像或體素分割。例如，Deepmedic對MRI的多模式腦腫瘤分割顯示出良好的性能[10]。但是，基于滑動窗口的方法效率低下，因為在許多窗口重疊的區域中會重復計算網絡功能。由于這個原因，基于滑動窗口的方法近被完全卷積網絡（FCN）取代[11]。關鍵思想是用卷積層和上采樣層替換分類網絡中的全連接層，這大大提G了分割效率。對于醫學圖像分割，諸如U-Net [12][13]之類的編碼器-解碼器網絡已顯示出令人鼓舞的性能。編碼器具有多個卷積和下采樣層，可提取不同比例的圖像特征。解碼器具有卷積和上采樣層，可恢復特征圖的空間分辨率，并終實現像素或體素密集分割。在[14]中可以找到有關訓練U-Net進行醫學圖像分割的不同歸一化方法的綜述。

對于內窺鏡胰管和膽道手術中的導航，Gibson等人 [15]使用膨脹的卷積和融合的圖像特征在多個尺度上分割來自CT掃描的腹部器官。為了從MRI進行胎盤和胎兒大腦的交互式分割，將FCN與用戶定義的邊界框和涂鴉結合起來，其中FCN的后幾層根據用戶輸入進行了微調[16]。手術器械界標的分割和定位被建模為熱圖回歸模型，并且使用FCN幾乎實時地跟蹤器械[17]。對于肺結節分割，Feng等通過使用候選篩選方法從弱標記的肺部CT中學習辨別區域來訓練FCN，解決了需要準確的手動注釋的問題[18]。Bai等提出了一種自我監督的學習策略，以有限的標記訓練數據來提GU-Net的心臟分割精度[19]。

4、配準

配準是兩個醫學圖像，體積或模態之間的空間對齊，這對于術前和術中規劃都特別重要。傳統算法通常迭代地計算參數轉換，即彈性，流體或B樣條曲線模型，以小化兩個醫療輸入之間的給定度量（即均方誤差，歸一化互相關或互信息）。近，深度回歸模型已被用來代替傳統的耗時和基于優化的注冊算法。

示例性的基于深度學習的配準方法包括VoxelMorph，它通過利用基于CNN的結構和輔助分割來將輸入圖像對映射到變形場，從而大化標準圖像匹配目標函數[20]。提出了一個用于3D醫學圖像配準的端到端深度學習框架，該框架包括三個階段：仿射變換預測，動量計算和非參數細化，以結合仿射配準和矢量動量參數化的固定速度場[21]。提出了一種用于多模式圖像配準的弱監督框架，該框架對具有較GJ別對應關系的圖像（即解剖標簽）進行訓練，而不是用于預測位移場的體素J別轉換[22]。每個馬爾科夫決策過程都由經過擴張的FCN訓練的代理商進行，以使3D體積與2D X射線圖像對齊[23]。RegNet是通過考慮多尺度背景而提出的，并在人工生成的位移矢量場（DVF）上進行了培訓，以實現非剛性配準[24]。3D圖像配準也可以公式化為策略學習過程，其中將3D原始圖像作為輸入，將下一個佳動作（即向上或向下）作為輸出，并將CNN作為代理[25]。

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