Multispectral imaging (MSI) could be useful for many applications in surgery, including tumor detection and perfusion monitoring. Acquisition of many bands however leads to long imaging times and/or low resolution, hampering widespread adoption of the technique. To overcome this issue, current research focusses on reducing the number of recorded bands. Yet, the methods proposed are not able to consider both the target domain (e.g. liver surgery) and the specific task (e.g. oxygenation or blood volume fraction monitoring) when selecting bands.
In this work we present the first approach to domain and task specific band selection. Our method relies on highly generic Monte Carlo-based tissue simulations that aim to capture a large range of optical tissue parameters potentially observed during surgical interventions. The adaptation of the model to a specific clinical application is based on label-free in vivo hyperspectral recordings using a recently published approach to multispectral domain adaptation. The bands are selected based on their performance to estimate a task-dependent physiological parameter. This performance is evaluated on the adapted simulations, which come with ground truth values. According to in vivo experiments with hyperspectral recordings of tumors in a mouse model, a small subset of bands is enough for accurate oxygenation and blood volume fraction estimation. Compared to state-of-the-art baseline methods, bands selected by our method show more accurate results in oxygenation estimation. Our work could thus help remove one of the last barriers for interventional usage of MSI.
Sebastian J. Wirkert, Fabian Isensee, Anant S. Vemuri, Klaus Maier-Hein, Baowei Fei, and Lena Maier-Hein, "Domain and task specific multispectral band selection (Conference Presentation)," Proc. SPIE 10486, Design and Quality for Biomedical Technologies XI, 104860H (Presented at SPIE BiOS: January 28, 2018; Published: 14 March 2018); https://doi.org/10.1117/12.2287824.5751458192001.
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