Dr. Rahul Prasanna Kumar Profile

Dr. Rahul Prasanna Kumar

at Oslo Univ Hospital

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 15 February 2021 Presentation + Paper

Modifying U-Net for small dataset: a simplified U-Net version for liver parenchyma segmentation

Pravda Jith Ray Prasad, Ole Jakob Elle, Frank Lindseth, Fritz Albregtsen, Rahul Prasanna Kumar

Proceedings Volume 11597, 115971O (2021) https://doi.org/10.1117/12.2582179

KEYWORDS: Image segmentation, Liver, Surgery, Medical imaging, Image processing, Data modeling, Network architectures, Binary data

Read Abstract +

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.

Proceedings Article | 13 March 2013 Paper

3D multiscale vessel enhancement based centerline extraction of blood vessels

Rahul Kumar, Fritz Albregtsen, Martin Reimers, Thomas Langø, Bjørn Edwin, Ole Jakob Elle

Proceedings Volume 8669, 86691X (2013) https://doi.org/10.1117/12.2006779

KEYWORDS: Blood vessels, Medical imaging, 3D image enhancement, Image processing, Image registration, Image segmentation, 3D image processing, Visualization, OSLO, Denoising

Read Abstract +

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.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

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.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

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.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years