3D segmentation of colorectal tumors

A deep learning strategy for the 3D segmentation of colorectal tumors from ultrasound imaging

Colorectal cancer remains a leading cause of cancer-related mortality worldwide, highlighting the need for accurate and efficient diagnostic tools. While Deep Learning has shown promise in medical imaging, its application to transrectal ultrasound for colorectal tumor segmentation remains underexplored. Currently, lesion segmentation is performed manually, relying on clinician expertise and leading to significant variability across treatment centers. To overcome this limitations, we propose a novel strategy that addresses both practical challenges and technical constraints, particularly in scenarios with limited data availability, offering a robust framework for accurate 3D colorectal tumor segmentation from ultrasound imaging.

We evaluate eight state-of-the-art models, including convolutional neural networks and transformer-based architectures, and introduce domain-tailored pre- and post-processing techniques such as data augmentation, patching and ensembling to enhance segmentation performance while reducing computational cost. Leading to an average improvement in term of DICE score of 0.423 absolute points (+107%), compared to baseline models, our findings demonstrate the potential of our proposal to improve the accuracy and reliability of ultrasound-based diagnostics for colorectal cancer, paving the way for clinically viable AI-driven solutions.

we proposed a novel Deep Learning-based strategy for 3D colorectal tumor segmentation in TRUS images. The primary and general contribution of our study was not to offer a fully resolved clinical solution, but rather to demonstrate a practical and effective strategy, more from a methodological perspective than a purely performance-oriented one, to address the challenges of limited data availability, acquisition heterogeneity, and resource-constrained clinical scenarios.

The integration of the various steps of our pipeline was intentionally developed to enhance robustness under such difficult conditions, to aim at enhancing diagnostic and decision support for colorectal cancer diagnosis. Through the proposed combination of techniques, we show that meaningful improvements in segmentation accuracy can be achieved even with very limited training data — an insight that may be valuable not only for 3D TRUS but also for other clinical scenarios facing similar data limitations. Other important contributions are in what follows.

Expanding datasets and refining models will be critical to enhancing clinical applicability. Indeed, a key limiting factor remains the severe scarcity of annotated 3D TRUS data for colorectal cancer segmentation, both in our specific case and more generally across a wide range of complex or rare imaging modalities. Similar challenges are commonly encountered in many other clinical domains characterized by limited data availability, such as rare diseases, expensive or operator-dependent imaging protocols, or underutilized modalities for which expert annotation requires highly specialized personnel.

With continued efforts to overcome these challenges, the development of AI-driven solutions for colorectal cancer diagnosis can lead to more accurate, standardized, and accessible diagnostics, ultimately improving patient care across diverse healthcare environments.

Ultrasound imaging, sonography, diagnostic ultrasound, medical imaging, prenatal scan, fetal ultrasound, Doppler ultrasound, abdominal ultrasound, pelvic ultrasound, transvaginal ultrasound, 3D ultrasound, 4D ultrasound, echocardiogram, ultrasound technician, ultrasound scan, soft tissue imaging, vascular ultrasound, ultrasound gel, real-time imaging, non-invasive scan

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