Abstract
In this study, a laparoscopic imaging device and a light source able to select wavelengths by bandpass filters were developed to perform multispectral imaging (MSI) using over 1000 nm near-infrared (OTN-NIR) on regions under a laparoscope. Subsequently, MSI (wavelengths: 1000–1400 nm) was performed using the built device on nine live mice before and after tumor implantation. The normal and tumor pixels captured within the mice were used as teaching data sets, and the tumor-implanted mice data were classified using a neural network applied following a leave-one-out cross-validation procedure. The system provided a specificity of 89.5%, a sensitivity of 53.5%, and an accuracy of 87.8% for subcutaneous tumor discrimination. Aggregated true-positive (TP) pixels were confirmed in all tumor-implanted mice, which indicated that the laparoscopic OTN-NIR MSI could potentially be applied in vivo for classifying target lesions such as cancer in deep tissues.
Highlights
IntroductionThe number of cancer cases and deaths worldwide has been strikingly increasing with the rapidly aging population [1]
Introduction published maps and institutional affilThe number of cancer cases and deaths worldwide has been strikingly increasing with the rapidly aging population [1]
We developed a laparoscopic imaging device and a wavelength-selectable light source to perform OTN-NIR multispectral imaging (MSI), which was performed on nine live mice
Summary
The number of cancer cases and deaths worldwide has been strikingly increasing with the rapidly aging population [1]. Surgical resection is one of the most common procedures used to treat cancers. As a minimally invasive surgery compared to laparotomy, laparoscopic surgery has been in great demand because the need for such surgeries has increased. In order to perform laparoscopic surgery more safely and efficiently, it is necessary to develop an image-guided surgical support system identifying cancerassociated regions and anatomical structures such as blood vessels and nerves. Recent advances in imaging technologies associated with machine learning have led to the identification of anatomical structures and the localization of cancer in surgical image data [2,3]. Red, green, and blue (RGB) images acquired by standard visiblelight-sensitive cameras only allow the observation of the target’s surface.
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