Toward complete colorectal tumor resection using intraoperative ultrasound and ensemble learning

Freija Geldof, Stijn Pruijssers, Lynn-Jade S. Jong, Dinusha Veluponnar, Theo Ruers, Behdad Dashtbozorg

Keywords: Colorectal cancer surgery, Ensemble learning, Data scarcity, Ultrasound

TL;DR: Ensemble of models pre-trained on breast ultrasound images for colorectal tumor detection in intraoperative ultrasound images

Abstract: Cancer surgery is characterized by a delicate balance between radical tumor resection and sparing healthy tissue and critical anatomical structures. The trouble of recognizing tissue structures during surgery may either lead to resection too close to the tumor resulting in tumor-positive resection margins or too wide resection around the tumor with potential damage to vital anatomical structures. Ultrasound is a widely available and non-invasive imaging technique which can be used for surgical guidance by continuous real-time tissue recognition during surgery, however, interpretation of US images requires training and experience. One of the notorious challenges in medical image analysis is the scarcity of labeled data. To address this issue, we introduce a deep ensemble learning framework for colorectal tumor detection in ultrasound images using models which are pre-trained for tumor segmentation in breast ultrasound images.

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Paper Type: novel methodological ideas without extensive validation

Primary Subject Area: Segmentation

Secondary Subject Area: Learning with Noisy Labels and Limited Data

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