Semi-Automatic Segmentation via Ultrasound Imaging: For AR-Guided Laparoscopic Liver Tumor Resections
Publicerad
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Liver cancer is one of the most prevalent and deadly forms of cancer. The primary
treatment for liver cancer is the surgical removal of the tumor, known as resection.
While traditionally performed via open surgery, there has been a significant
shift towards minimally invasive surgery, which involves smaller incisions and offers
advantages such as faster recovery and shorter hospital stays. Despite its benefits,
minimally invasive surgery presents challenges in distinguishing between tumor
tissue and healthy liver tissue. An effective solution to these challenges is an augmented
reality guiding tool that assists surgeons by overlaying a 3D model of the
tumor onto the laparoscopic camera view during surgery.
This master’s thesis, conducted in collaboration with Navari Surgical, aims to develop
a semi-automatic segmentation method based on ultrasound imaging for creating
such a 3D tumor model. The method involves preprocessing techniques and a
segmentation algorithm implemented in Python. The evaluation, based on relevant
metrics, demonstrates that the method performs well with images of high tumor visibility,
though challenges remain with images containing multiple tumor areas and
those requiring individualized preprocessing parameters. Future work should focus
on refining these aspects and incorporating machine learning models to enhance
accuracy and usability. This study establishes the feasibility of using ultrasound
imaging for liver tumor segmentation and provides a foundation for further research
in this area.
Beskrivning
Ämne/nyckelord
Segmentation, ultrasound, liver tumor, laparoscopy, augmented reality, active contour models.