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Scribble2D5: Weakly-Supervised Volumetric Image Segmentation via Scribble Annotations [article]

Qiuhui Chen, Yi Hong
<span title="2022-05-13">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we propose a scribble-based volumetric image segmentation, Scribble2D5, which tackles 3D anisotropic image segmentation and improves boundary prediction.  ...  Recently, weakly-supervised image segmentation using weak annotations like scribbles has gained great attention, since such annotations are much easier to obtain compared to time-consuming and label-intensive  ...  To address the boundary localization issue, we propose use the combination of learning both static and active boundaries via predicting edges in 3D and a proposed active boundary loss in 3D based on active  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.06779v1">arXiv:2205.06779v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/o7y6ha62zbbiri3yfiy4mscfay">fatcat:o7y6ha62zbbiri3yfiy4mscfay</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220518100926/https://arxiv.org/pdf/2205.06779v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3d/c8/3dc896ec6f8f63e3a3c01df8fb1d396ce3a9192f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.06779v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Weakly supervised segmentation from extreme points [article]

Holger Roth, Ling Zhang, Dong Yang, Fausto Milletari, Ziyue Xu, Xiaosong Wang, Daguang Xu
<span title="2019-10-02">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We use extreme points in each dimension of a 3D medical image to constrain an initial segmentation based on the random walker algorithm.  ...  This segmentation is then used as a weak supervisory signal to train a fully convolutional network that can segment the organ of interest based on the provided user clicks.  ...  Initial segmentation from scribbles via random walker algorithm 3. Segmentation via deep fully convolutional network 4.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.01236v1">arXiv:1910.01236v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/joub4vndynfllbhkqma6fsdhim">fatcat:joub4vndynfllbhkqma6fsdhim</a> </span>
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Weakly Supervised Volumetric Segmentation via Self-taught Shape Denoising Model [article]

Qian He, Shuailin Li, Xuming He
<span title="2021-05-06">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To address this, we propose a novel weakly-supervised segmentation strategy capable of better capturing 3D shape prior in both model prediction and learning.  ...  Weakly supervised segmentation is an important problem in medical image analysis due to the high cost of pixelwise annotation.  ...  We introduce self-taught learning to weakly supervised segmentation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2104.13082v2">arXiv:2104.13082v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gbkdduumrraldglhprpow7hp7q">fatcat:gbkdduumrraldglhprpow7hp7q</a> </span>
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Going to Extremes: Weakly Supervised Medical Image Segmentation

Holger R. Roth, Dong Yang, Ziyue Xu, Xiaosong Wang, Daguang Xu
<span title="2021-06-02">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tjwucdga6zfftlebfsmbvxjiyy" style="color: black;">Machine Learning and Knowledge Extraction</a> </i> &nbsp;
This initial segmentation is then used as a noisy supervision signal to train a fully convolutional network that can segment the organ of interest, based on the provided user clicks.  ...  An initial segmentation is generated based on the extreme points using the random walker algorithm.  ...  Summary In summary, we proposed a weakly supervised 3D segmentation framework based on extreme point clicks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/make3020026">doi:10.3390/make3020026</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vpy3rtl63rctjcv3sgtd7mn56u">fatcat:vpy3rtl63rctjcv3sgtd7mn56u</a> </span>
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Going to Extremes: Weakly Supervised Medical Image Segmentation [article]

Holger R Roth, Dong Yang, Ziyue Xu, Xiaosong Wang, Daguang Xu
<span title="2020-09-25">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This initial segmentation is then used as a noisy supervision signal to train a fully convolutional network that can segment the organ of interest, based on the provided user clicks.  ...  An initial segmentation is generated based on the extreme points utilizing the random walker algorithm.  ...  In summary, we proposed a weakly-supervised 3D segmentation framework based on extreme point clicks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2009.11988v1">arXiv:2009.11988v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/msm35eefarharcaldcmjhr3wzm">fatcat:msm35eefarharcaldcmjhr3wzm</a> </span>
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One-shot Weakly-Supervised Segmentation in Medical Images [article]

Wenhui Lei, Qi Su, Ran Gu, Na Wang, Xinglong Liu, Guotai Wang, Xiaofan Zhang, Shaoting Zhang
<span title="2021-11-21">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Hence, we present an innovative framework for 3D medical image segmentation with one-shot and weakly-supervised settings.  ...  One-shot segmentation and weakly-supervised learning are promising research directions that lower labeling effort by learning a new class from only one annotated image and utilizing coarse labels instead  ...  We go through all the points in scribble and achieve the final propagated results. 3) With the pseudo scribbles, we apply geodesic distance-based weakly supervised segmentation method [9, 57] generating  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2111.10773v1">arXiv:2111.10773v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bzryc4hkqnabbifopr3icimdku">fatcat:bzryc4hkqnabbifopr3icimdku</a> </span>
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Inter Extreme Points Geodesics for Weakly Supervised Segmentation [article]

Reuben Dorent, Samuel Joutard, Jonathan Shapey, Aaron Kujawa, Marc Modat, Sebastien Ourselin, Tom Vercauteren
<span title="2021-07-01">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We introduce InExtremIS, a weakly supervised 3D approach to train a deep image segmentation network using particularly weak train-time annotations: only 6 extreme clicks at the boundary of the objects  ...  InExtremIS obtained competitive performance, approaching full supervision and outperforming significantly other weakly supervised techniques based on bounding boxes.  ...  Related work Weakly supervised image segmentation Weakly supervised learning covers a large variety of techniques that aim to build predictive models using time-efficient weak annotations.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.00583v1">arXiv:2107.00583v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ncozlnfitzdgbmd5r6t7qy635y">fatcat:ncozlnfitzdgbmd5r6t7qy635y</a> </span>
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Point-supervised Segmentation of Microscopy Images and Volumes via Objectness Regularization [article]

Shijie Li, Neel Dey, Katharina Bermond, Leon von der Emde, Christine A. Curcio, Thomas Ach, Guido Gerig
<span title="2021-03-19">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Finally, we scale our methodology to point-supervised segmentation in 3D fluorescence microscopy volumes, obviating the need for arduous manual volumetric delineation. Our code is freely available.  ...  learning from the constructed soft-labels.  ...  Most recently, [9] uses auxiliary networks to learn edge information via a Sobel-filter based loss.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2103.05617v2">arXiv:2103.05617v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/h3phricp6jfqrcynsqxf2e4vui">fatcat:h3phricp6jfqrcynsqxf2e4vui</a> </span>
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Joint Supervoxel Classification Forest for Weakly-Supervised Organ Segmentation [chapter]

Fahdi Kanavati, Kazunari Misawa, Michitaka Fujiwara, Kensaku Mori, Daniel Rueckert, Ben Glocker
<span title="">2017</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
We demonstrate the effectiveness of our approach on a dataset of 150 abdominal CT images where, starting from a small set of 10 images with scribbles, we perform weakly-supervised image segmentation of  ...  This article presents an efficient method for weakly-supervised organ segmentation. It consists in over-segmenting the images into objectlike supervoxels.  ...  To investigate whether weak supervised segmentation can be performed efficiently on a large 3D medical dataset, we make use of random forests, which are one of the most popular supervised machine learning  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-67389-9_10">doi:10.1007/978-3-319-67389-9_10</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ij6tdpd3pvfxxjvnsuf66d7tiy">fatcat:ij6tdpd3pvfxxjvnsuf66d7tiy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180722000915/http://spiral.imperial.ac.uk/bitstream/10044/1/51145/2/kanavati2017mlmi.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/47/45/47450bfb9561a9836c6b42016fbae70cd1af7eee.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-67389-9_10"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Dense Supervision Propagation for Weakly Supervised Semantic Segmentation on 3D Point Clouds [article]

Jiacheng Wei, Guosheng Lin, Kim-Hui Yap, Fayao Liu, Tzu-Yi Hung
<span title="2021-09-03">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
While dense labeling on 3D data is expensive and time-consuming, only a few works address weakly supervised semantic point cloud segmentation methods to relieve the labeling cost by learning from simpler  ...  Semantic segmentation on 3D point clouds is an important task for 3D scene understanding.  ...  Weakly supervised 3D semantic segmentation Existing 3D WSSS methods utilize different kinds of weak supervisions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.11267v2">arXiv:2107.11267v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cal4ywchdvfbhhizuq6lrantbq">fatcat:cal4ywchdvfbhhizuq6lrantbq</a> </span>
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Front Matter: Volume 12032

Ivana Išgum, Olivier Colliot
<span title="2022-04-25">2022</span> <i title="SPIE"> Medical Imaging 2022: Image Processing </i> &nbsp;
.  The last two digits indicate publication order within the volume using a Base 36 numbering system employing both numerals and letters. These two-number sets start with 00,  ...  ungated chest CT scans using convolutional long-short term memory networks [12032-115] 3B Weakly supervised brain tumor segmentation via semantic affinity deep neural network [12032-116] 3C Investigation  ...  a semi-supervised deep learning strategy [12032-93] 2P Automatic 2D to 3D localization of histological mouse brain sections in the reference atlas using deep learning [12032-94] 2Q Automated quality check  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1117/12.2638192">doi:10.1117/12.2638192</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ikfgnjefaba2tpiamxoftyi6sa">fatcat:ikfgnjefaba2tpiamxoftyi6sa</a> </span>
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Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models [article]

Jialin Peng, Ye Wang
<span title="2021-02-28">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
However, due to its intrinsic difficulty, segmentation with limited supervision is challenging and specific model design and/or learning strategies are needed.  ...  application of deep learning models in medical image segmentation.  ...  A similar idea has been adopted in [185] to weakly supervised segmentation of covid-19 in CT images. For semi-supervised medical image segmentation, Peng et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2103.00429v1">arXiv:2103.00429v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/p44a5e34sre4nasea5kjvva55e">fatcat:p44a5e34sre4nasea5kjvva55e</a> </span>
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3D Guided Weakly Supervised Semantic Segmentation [article]

Weixuan Sun, Jing Zhang, Nick Barnes
<span title="2020-12-01">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we propose a weakly supervised 2D semantic segmentation model by incorporating sparse bounding box labels with available 3D information, which is much easier to obtain with advanced sensors  ...  Pixel-wise clean annotation is necessary for fully-supervised semantic segmentation, which is laborious and expensive to obtain.  ...  Proposed Approach We propose a 3D information guided bounding-box based weakly supervised semantic segmentation network.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2012.00242v1">arXiv:2012.00242v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ruki4paggzdxzawncsl2gk62fy">fatcat:ruki4paggzdxzawncsl2gk62fy</a> </span>
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Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models

Jialin Peng, Ye Wang
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
INDEX TERMS Medical image segmentation, semi-supervised segmentation, partially-supervised segmentation, noisy label, sparse annotation. 36828  ...  However, due to its intrinsic difficulty, segmentation with limited supervision is challenging and specific model design and/or learning strategies are needed.  ...  A similar idea has been adopted in [185] to weakly supervised segmentation of covid-19 in CT images. For semi-supervised medical image segmentation, Peng et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3062380">doi:10.1109/access.2021.3062380</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/r5vsec2yfzcy5nk7wusiftyayu">fatcat:r5vsec2yfzcy5nk7wusiftyayu</a> </span>
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Learning to segment microscopy images with lazy labels [article]

Rihuan Ke, Aurélie Bugeau, Nicolas Papadakis, Peter Schuetz, Carola-Bibiane Schönlieb
<span title="2020-09-10">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for segmenting bioimages that can contain numerous object instances with thin separations  ...  These tasks are learned in an end-to-end multi-task learning framework.  ...  Weakly supervised learning and multi-task learning Standard supervision for semantic segmentation relies on a set of image and ground truth segmentation pairs.  ... 
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