Automation in the field of medical image segmentation is critical in helping the oncologists and surgeons for the accurate analysis of several pathological conditions by saving time. The ability to automatically segment the liver fast and accurately enables clinicians to understand the anatomical structure of the organ, and helps in the decision making process of diagnosis, surgery planning and as an anatomical map during surgical navigation especially important when using intraoperative image modalities . This work aims to develop an automatic liver parenchyma segmentation network which is based on U-Net architecture, a widely used architecture for medical image segmentation. This modified U-Net architecture includes reduced convolutional layers and using dropout layers as well as pre-processing the dataset to overcome the constraints of a small sample set. Reduced architecture complexity and introducing dropout regularization, addresses the problem of overfitting. We experimented with a callback for observing the training failure where it follows the early stopping policy and selecting the best model. Adding Gaussian noise to data can help the model to generalise well. For choosing the appropriate loss function we tested four different loss functions; Dice, binary cross entropy, Tversky and focal Tversky and concluded that Dice performs better. The network has been trained and validated using publicly available 3D-IRCADb dataset with images from 20 patients and achieved an overall Dice score of 94.5%. The overall objective of this work is to construct a network from a small sample set without the problem of overfitting or under-fitting, but delivering an acceptable Dice score.
Extraction of blood vessel structure is important for improving planning, navigation and tracking in several
interventional procedures. Centerline based registration methods have proven to be fast for clinical applications and an
effective way of registering multi-modal images. Here, we present a novel blood vessel centerline extraction method in
3D. Our method consists of two parts, namely Multiscale Vessel Enhancement Filtering (MVEF) and Centerline
Extraction using Vessel Direction (CEVD). Our proposed MVEF has an improved noise reduction and better Gaussian
profile at the vessel cross-sections compared to conventional MVEF. The CEVD is our novel method for tracing the
peaks of the Gaussian profile of the local MVEF at the vessel cross-sections. The peak of the Gaussian profile provides
the center position of the blood vessels. The novelty of this method is in effectively finding only the connected
centerlines of the blood vessels of interest. The proposed method was evaluated using both synthetic and medical
images. On comparing with Frangi's vesselness filtering combined with thinning, our method is shown to be
approximately 5 times faster. The results also show that our method is customized to detect only the desired blood
vessels, thereby eliminating the detection of unwanted vessel-like structures. The centerline accuracy was evaluated by
comparing with ground truth data created by finding Hough circle centers at each cross-section of the vessel structure.
The modified symmetric Hausdorff distance between our result and the ground truth was approximately 1 pixel for both
synthetic and medical images.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.