Laparoscopic surgery is a minimally invasive way of cancer resection, which is expected to increase in number. However, because a typical laparoscope can only receive visible light, there is a risk of accidentally damaging nerves that are similar in color to other tissues. To solve this problem, near-infrared (NIR) light (approximately 700-2,500 nm) is considered to be effective because of its feature; component analysis based on the molecular vibrations specific to biomolecules. Previously, we have developed NIR multispectral imaging (MSI) laparoscopy, which acquires NIR spectrum at 14 wavelengths with a band-pass filter. However, since the wavelength is limited, the optimal wavelength for identification cannot be studied. In this study, we developed the world's first laparoscopic device capable of NIR hyperspectral imaging (HSI) with an increased number of wavelengths. Furthermore, NIR-HSI was conducted in a living pig, and the machine-learning was demonstrated to identify nerves and other tissues; accuracy was 0.907.
Two main features of near-infrared (NIR) light are the ability to perform component analysis based on spectral differences and to have permeability to biological tissues. These features make the technology to acquire NIR spectral of the deep lesion and analyze the components by each pixel, called hyperspectral imaging (HSI). Mounting this technology to a laparoscope enables visualization of invisible or looking-similar tissues in visible light during laparoscopic surgery. In this research, the developed NIR-HSI laparoscopic device acquired NIR spectrum images on in vivo pig specimens. Through the experiments, the difference in spectrum between the artery and surrounding other tissues was confirmed. Additionally, a machine learning procedure provided high accuracy detection of the artery area; accuracy, precision, and recall are 0.868 %, 0.921 %, and 0.637 % respectively.
Automatically segmenting bleeding regions is an essential task for computer-assisted surgery systems. Recent development in deep learning has boosted the performance of medical image segmentation. However, training deep neural networks, in general, requires high-quality pixel-wise annotations, in which such an annotating process is expensive and easy to introduce annotation noise. To address this issue, motivated by the observation that the noise should be data-dependent, we propose an uncertainty-guided label smoothing method instead of using a fixed label smoothing strategy. Aleatoric uncertainty, which accounts for inherent noise such as annotation error, is estimated via an additional branch of deep neural networks. With the help of estimated aleatoric uncertainty, we could guide the spatial label smoothing in a self-adaptive manner. We demonstrated the effectiveness of our proposal by evaluating the Dice coefficient in a private bleeding segmentation dataset. An improvement over the baseline was observed for our proposal.
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