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G-SET-DCL: a guided sequential episodic training with dual contrastive learning approach for colon segmentation.
Int J Comput Assist Radiol Surg
January 2025
Computer Vision and Image Processing Lab., UofL, Louisville, KY, 40292, USA.
Purpose: This article introduces a novel deep learning approach to substantially improve the accuracy of colon segmentation even with limited data annotation, which enhances the overall effectiveness of the CT colonography pipeline in clinical settings.
Methods: The proposed approach integrates 3D contextual information via guided sequential episodic training in which a query CT slice is segmented by exploiting its previous labeled CT slice (i.e.
Screening Colonoscopy to Reduce the Incidence and Mortality of Colorectal Cancer.
Digestion
October 2024
Department of Endoscopy, The Jikei University School of Medicine, Tokyo, Japan.
Background: Colorectal cancer (CRC) is a major concern because of its increasing incidence and mortality worldwide. Therefore, effective screening strategies are necessary to reduce its incidence.
Summary: In addition to fecal immunochemical tests and computed tomography colonography, screening colonoscopy is expected to significantly contribute to the reduction of CRC.
Current sessile serrated lesion incidence: implications for future clinical practice.
ANZ J Surg
October 2024
Department of Surgery and Critical Care, University of Otago, Christchurch, New Zealand.
Article Synopsis
- Sessile serrated lesions (SSL) are linked to up to 30% of colorectal cancers, and their true incidence may have been underestimated due to past classification and colonoscopy limitations.
- A study conducted in Canterbury, New Zealand, over one year reviewed colonoscopy records and found that 18.6% of patients had at least one SSL excised among 4,346 procedures.
- Results indicated that SSLs comprised 24% of all polyps removed, were mostly found in the right colon, and were typically smaller than 10 mm, suggesting SSL detection should be prioritized in future screenings.
Depth estimation from monocular endoscopy using simulation and image transfer approach.
Comput Biol Med
October 2024
Department of Health Sciences and Technology, GAIHST, Gachon University, Incheon, 21999, South Korea; Department of Biomedical Engineering, Gachon University, Seongnam, 13120, South Korea. Electronic address:
Article Synopsis
- Accurate depth information in endoscopy is critical for effective navigation, but space limitations make it hard to use depth cameras.
- The study proposes a three-step process to estimate depth from endoscopic images using deep learning, starting with creating simulated images and depth maps using Unity.
- The resulting depth estimation method shows improved accuracy compared to previous unsupervised approaches, offering potential benefits for navigation and clinical outcomes in endoscopy.
Unveiling Colorectal Cancer Biomarkers: Harnessing Biosensor Technology for Volatile Organic Compound Detection.
Sensors (Basel)
July 2024
Laboratory of Cell Technology, Department of Biotechnology, Agricultural University of Athens, EU-CONEXUS European University, 11855 Athens, Greece.
Conventional screening options for colorectal cancer (CRC) detection are mainly direct visualization and invasive methods including colonoscopy and flexible sigmoidoscopy, which must be performed in a clinical setting and may be linked to adverse effects for some patients. Non-invasive CRC diagnostic tests such as computed tomography colonography and stool tests are either too costly or less reliable than invasive ones. On the other hand, volatile organic compounds (VOCs) are potentially ideal non-invasive biomarkers for CRC detection and monitoring.
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