Evidence map›Paper›PMID 30636936›Full record

ArticleProceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)2018

A NOVEL IMAGE-SPECIFIC TRANSFER APPROACH FOR PROSTATE SEGMENTATION IN MR IMAGES.

Pinzhuo Tian, Lei Qi, Yinghuan Shi, Luping Zhou, Yang Gao, Dinggang Shen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Pinzhuo TianState Key Laboratory for Novel Software Technology, Nanjing University, China.
Lei QiState Key Laboratory for Novel Software Technology, Nanjing University, China.
Yinghuan ShiState Key Laboratory for Novel Software Technology, Nanjing University, China.
Luping ZhouSchool of Computing and Information Technology, University of Wollongong, Australia.
Yang GaoState Key Laboratory for Novel Software Technology, Nanjing University, China.
Dinggang ShenDepartment of Radiology and BRIC, UNC Chapel Hill, USA.

Funding

Automatic Pelvic Organ Delineation in Prostate Cancer TreatmentR01CA206100 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LIAN, JUN · 2016 to 2022
$1.7M
NCI NIH HHS R01 CA206100
6 · The paper itself

Abstract

Prostate segmentation in Magnetic Resonance (MR) Images is a significant yet challenging task for prostate cancer treatment. Most of the existing works attempted to design a global classifier for all MR images, which neglect the discrepancy of images across different patients. To this end, we propose a novel transfer approach for prostate segmentation in MR images. Firstly, an image-specific classifier is built for each training image. Secondly, a pair of dictionaries and a mapping matrix are jointly obtained by a novel Semi-Coupled Dictionary Transfer Learning (SCDTL). Finally, the classifiers on the source domain could be selectively transferred to the target domain (

Indexed as

Image-Specific Transfer ApproachProstate SegmentationSemi-Coupled Dictionary Transfer Learning

Identifiers

PMID30636936
PMCPMC6328258

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.