Comparison and Validation of Fast Organ Detection Methods on Ultrasound Images Using CNN

  • IGARASHI Riki
    Graduate School of Informatics and Engineering, The University of Electro-Communications
  • TOMITA Kyohei
    Graduate School of Informatics and Engineering, The University of Electro-Communications
  • NISHIYAMA Yu
    Graduate School of Informatics and Engineering, The University of Electro-Communications
  • KOIZUMI Norihiro
    Graduate School of Informatics and Engineering, The University of Electro-Communications

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  • CNNを用いた高速な超音波画像上の臓器検出手法の比較・検証
  • CNN オ モチイタ コウソク ナ チョウオンパ ガゾウ ジョウ ノ ゾウキ ケンシュツ シュホウ ノ ヒカク ・ ケンショウ

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Abstract

<p>We are developing a system to treat kidney, liver and other organs, as well as stones and cancer in these organs using high-power focused ultrasound (HIFU) while tracking lesions that move by breathing and body movements. The system estimates organ movement by analyzing ultrasound images obtained from the probe and compensates for this movement with robotic control. In recent years, various medical image analysis methods using deep learning have been proposed and studied, but they have not been fully explored as methods to detect specific organs using robotic systems. In this paper, we compared and validated the performance of Faster R-CNN, which is commonly used for object detection, and the proposed methods, Regression Network (RegNet) and Segmentation In Regression Network (SegInRegNet), the proposed method based on the problems of Faster R-CNN, in a kidney detection task. And then, we show that 1) there are some problems with Faster R-CNN as a kidney detection method operating on a robotic system and that 2) proposed method performs better than Faster R-CNN in terms of detection speed and accuracy.</p>

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