RNN-LSTM based Tissue Classification in Robotic System for Breast Biopsy

Naveen Kumar Sankaran, Thenkurussi Kesavadas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Objective: Accurate needle tip placement for biopsy procedures is difficult to achieve with low-fidelity imaging systems. Conventionally, surgeons while performing biopsies rely on ultrasound images and intuitive feeling about needle-tissue interaction forces to confirm target location. Currently, robotic assistance for biopsy uses only the position parameter to address localization challenges. In the present work, in addition to a robot's position sense, we propose to integrate needle-tissue force parameter. This force model presents a new way to built an intelligent robot that can identify tissue properties during needle probing/biopsy. Methods: A standard experiment was setup that consist of a force sensor, a linear stage, a biopsy needle, and synthetic tissues. During the experiment, needle penetrates through synthetic tissues, a set of data (force and distance) was acquired and manually labeled. A recurrent neural network (RNN) based Long-Short Term Memory (LSTM) model was trained with the data to estimate the various classes (air, skin/fibrous tissue, puncture, and hard tissue). Result: The trained model is able to distinguish between the three synthetic materials. Intuitively, this model mimics human perceptions of force during a handheld needle penetration. Conclusion: The constrained experimental setup helps us present a proof of concept for using deep learning models for tissue classification. Significance: Tissue classification is the first step towards solving the more difficult problem of developing a robotic device capable of precise event detection of tissue transitions.

Original languageEnglish (US)
Title of host publication2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020
PublisherIEEE Computer Society
Pages846-852
Number of pages7
ISBN (Electronic)9781728159072
DOIs
StatePublished - Nov 2020
Event8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020 - New York City, United States
Duration: Nov 29 2020Dec 1 2020

Publication series

NameProceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
Volume2020-November
ISSN (Print)2155-1774

Conference

Conference8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020
CountryUnited States
CityNew York City
Period11/29/2012/1/20

Keywords

  • artificial intelligence
  • breast biopsy robot
  • Interventional surgical procedure
  • needle penetration force
  • tissue classification

ASJC Scopus subject areas

  • Artificial Intelligence
  • Biomedical Engineering
  • Mechanical Engineering

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