A computational model for estimating tumor margins in complementary tactile and 3D ultrasound images

Arefin Shamsil; Abelardo Escoto; Michael D. Naish; Rajni V. Patel

doi:10.1117/12.2217311

18 March 2016 A computational model for estimating tumor margins in complementary tactile and 3D ultrasound images

Arefin Shamsil, Abelardo Escoto, Michael D. Naish, Rajni V. Patel

Proceedings Volume 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling; 978616 (2016) https://doi.org/10.1117/12.2217311
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Conventional surgical methods are effective for treating lung tumors; however, they impose high trauma and pain to patients. Minimally invasive surgery is a safer alternative as smaller incisions are required to reach the lung; however, it is challenging due to inadequate intraoperative tumor localization. To address this issue, a mechatronic palpation device was developed that incorporates tactile and ultrasound sensors capable of acquiring surface and cross-sectional images of palpated tissue. Initial work focused on tactile image segmentation and fusion of position-tracked tactile images, resulting in a reconstruction of the palpated surface to compute the spatial locations of underlying tumors. This paper presents a computational model capable of analyzing orthogonally-paired tactile and ultrasound images to compute the surface circumference and depth margins of a tumor. The framework also integrates an error compensation technique and an algebraic model to align all of the image pairs and to estimate the tumor depths within the tracked thickness of a palpated tissue. For validation, an ex vivo experimental study was conducted involving the complete palpation of 11 porcine liver tissues injected with iodine-agar tumors of varying sizes and shapes. The resulting tactile and ultrasound images were then processed using the proposed model to compute the tumor margins and compare them to fluoroscopy based physical measurements. The results show a good negative correlation (r = −0.783, p = 0.004) between the tumor surface margins and a good positive correlation (r = 0.743, p = 0.009) between the tumor depth margins.

Citation Download Citation

Arefin Shamsil, Abelardo Escoto, Michael D. Naish, and Rajni V. Patel "A computational model for estimating tumor margins in complementary tactile and 3D ultrasound images", Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 978616 (18 March 2016); https://doi.org/10.1117/12.2217311

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