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學術演講

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TIGP (BIO)—Classification of Blood Loss from Prostate Surgery Videos Using Convolutional Neural Network

  • 講者洪哲倫 教授 (國立陽明交通大學 生物醫學資訊研究所)
    邀請人:TIGP (Bioinformatics)
  • 時間2021-03-25 (Thu.) 14:00 – 16:00
  • 地點資訊所新館101演講廳
摘要

Most older men have verified that their prostate gland will constantly grow bigger in size, which is known as benign prostatic hyperplasia (BPH). An enlarged prostate will lead to the blockage of the bladder outlet and prostatic channel. This condition may cause some urination issues. Those with obstructive prostate symptoms can optionally select the transurethral resection of the prostate (TURP) as treatment. Transurethral resection of the prostate (TURP) is a surgical removal of obstructing prostate tissue. The total bleeding area is used to determine the performance of the TURP surgery. Although the traditional method for the detection of bleeding areas provides accurate results, it cannot detect them in time for surgery diagnosis. Moreover, it is easily disturbed to judge bleeding areas for experienced physicians because a red light pattern arising from the surgical cutting loop often appears on the images. Recently, the automatic computer-aided technique and artificial intelligence deep learning are broadly used in medical image recognition, which can effectively extract the desired features to reduce the burden of physicians and increase the accuracy of diagnosis. In this study, the four levels of the bleeding area ratio (BAR) were defined as 0%–25%, 25%–50%, 50%–75%, and 75%–100%, which corresponded to the original image/bleeding area image. The convolutional neural network techniques are used for classify the four levels of the blood loss in the TURP surgery. This study presented an effective recognition approach to calculate the bleeding area of the TURP surgery, and it could provide clinicians with a useful assessment of surgical performance in diagnosis.