Shape-Aware Organ Segmentation by Predicting Signed Distance Maps
نویسندگان
چکیده
منابع مشابه
Shape-aware Instance Segmentation
We address the problem of instance-level semantic segmentation, which aims at jointly detecting, segmenting and classifying every individual object in an image. In this context, existing methods typically propose candidate objects, usually as bounding boxes, and directly predict a binary mask within each such proposal. As a consequence, they cannot recover from errors in the object candidate ge...
متن کاملSigned Lp-distance fields
We introduce and study a family of generalized double-layer potentials which are used to build smooth and accurate approximants for the signed distance function. Given a surface, the value of an approximant at a given point is a power mean of distances from the point to the surface points parameterized by the angle they are viewed from the given point. We analyze mathematical properties of the ...
متن کاملTruncated Signed Distance Fields
This thesis is concerned with topics related to dense mapping of large scale three-dimensional spaces. In particular, the motivating scenario of this work is one in which a mobile robot with limited computational resources explores an unknown environment using a depth-camera. To this end, low-level topics such as sensor noise, map representation, interpolation, bit-rates, compression are invest...
متن کاملUsing Probability Maps for Multi-organ Automatic Segmentation
Organ segmentation is a vital task in diagnostic medicine. The ability to perform it automatically can save clinicians time and labor. In this paper, a method to achieve automatic segmentation of organs in three–dimensional (3D), non–annotated, full–body magnetic resonance (MR), and computed tomography (CT) volumes is proposed. According to the method, training volumes are registered to a chose...
متن کاملA Generic Probabilistic Active Shape Model for Organ Segmentation
Probabilistic models are extensively used in medical image segmentation. Most of them employ parametric representations of densities and make idealizing assumptions, e.g., normal distribution of data. Often, such assumptions are inadequate and limit a broader application. We propose here a novel probabilistic active shape model for organ segmentation, which is entirely built upon non-parametric...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2020
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v34i07.6946