Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation

Medical Physics
Han Sang LeeDae Chul Jung

Abstract

To develop an automatic deep feature classification (DFC) method for distinguishing benign angiomyolipoma without visible fat (AMLwvf) from malignant clear cell renal cell carcinoma (ccRCC) from abdominal contrast-enhanced computer tomography (CE CT) images. A dataset including 80 abdominal CT images of 39 AMLwvf and 41 ccRCC patients was used. We proposed a DFC method for differentiating the small renal masses (SRM) into AMLwvf and ccRCC using the combination of hand-crafted and deep features, and machine learning classifiers. First, 71-dimensional hand-crafted features (HCF) of texture and shape were extracted from the SRM contours. Second, 1000-4000-dimensional deep features (DF) were extracted from the ImageNet pretrained deep learning model with the SRM image patches. In DF extraction, we proposed the texture image patches (TIP) to emphasize the texture information inside the mass in DFs and reduce the mass size variability. Finally, the two features were concatenated and the random forest (RF) classifier was trained on these concatenated features to classify the types of SRMs. The proposed method was tested on our dataset using leave-one-out cross-validation and evaluated using accuracy, sensitivity, specificity, positive...Continue Reading

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Citations

Oct 16, 2019·AJR. American Journal of Roentgenology·Burak KocakOzgur Kilickesmez
Jan 9, 2020·AJR. American Journal of Roentgenology·Takashi TanakaSusumu Kanazawa
Jun 3, 2020·Cancers·Rodrigo Suarez-IbarrolaArkadiusz Miernik
Jul 22, 2018·Der Radiologe·Hubert Stefan Bickel
Aug 14, 2020·AJR. American Journal of Roentgenology·Burak KocakOzgur Kilickesmez
Jan 27, 2021·World Journal of Urology·Teele KuuskAxel Bex
Apr 22, 2021·Current Opinion in Urology·Matthew LeeAlexander Kutikov
Aug 3, 2021·Radiological Physics and Technology·Tomoko MaruyamaUNKNOWN Alzheimer’s Disease Neuroimaging Initiative

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