IntellEditS: Intelligent Learning-Based Editor of Segmentations

Harrison, Adam P.; Birkbeck, Neil; Sofka, Michal

doi:10.1007/978-3-642-40760-4_30

Cited by 7 publications

(8 citation statements)

References 16 publications

(33 reference statements)

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“…Classic approaches, like the random walker (RW) algorithm [5], do so via propagating seed regions using intensity similarities. Later approaches add additional constraints, e.g., based on presegmentations [6] or learned probabilities [7]. With the advent of deep-learning, harmonizing mask predictions with the UIs continues to be a challenge.…”

Section: Introductionmentioning

confidence: 99%

User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation

Raju¹,

Ji²,

Cheng³

et al. 2020

Preprint

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Mask-based annotation of medical images, especially for 3D data, is a bottleneck in developing reliable machine learning models. Using minimal-labor user interactions (UIs) to guide the annotation is promising, but challenges remain on best harmonizing the mask prediction with the UIs. To address this, we propose the user-guided domain adaptation (UGDA) framework, which uses prediction-based adversarial domain adaptation (PADA) to model the combined distribution of UIs and mask predictions. The UIs are then used as anchors to guide and align the mask prediction. Importantly, UGDA can both learn from unlabelled data and also model the high-level semantic meaning behind different UIs. We test UGDA on annotating pathological livers using a clinically comprehensive dataset of 927 patient studies. Using only extreme-point UIs, we achieve a mean (worst-case) performance of 96.1% (94.9%), compared to 93.0% (87.0%) for deep extreme points (DEXTR). Furthermore, we also show UGDA can retain this state-of-the-art performance even when only seeing a fraction of available UIs, demonstrating an ability for robust and reliable UI-guided segmentation with extremely minimal labor demands.

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Section: Introductionmentioning

confidence: 99%

User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation

Raju¹,

Ji²,

Cheng³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Graph Cuts [2] and Random Walks [5] learn a probability model from user-provided scribbles drawn on the foreground and background. To achieve a good interaction efficiency, machine learning methods [11,6,10] have been used to learn image features from the user inputs with reduced amount of interaction.…”

Section: Introductionmentioning

confidence: 99%

Dynamically Balanced Online Random Forests for Interactive Scribble-Based Segmentation

Wang

Zuluaga

Pratt

et al. 2016

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016

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Abstract. Interactive scribble-and-learning-based segmentation is attractive for its good performance and reduced number of user interaction. Scribbles for foreground and background are often imbalanced. With the arrival of new scribbles, the imbalance ratio may change largely. Failing to deal with imbalanced training data and a changing imbalance ratio may lead to a decreased sensitivity and accuracy for segmentation. We propose a generic Dynamically Balanced Online Random Forest (DyBa ORF) to deal with these problems, with a combination of a dynamically balanced online Bagging method and a tree growing and shrinking strategy to update the random forests. We validated DyBa ORF on UCI machine learning data sets and applied it to two different clinical applications: 2D segmentation of the placenta from fetal MRI and adult lungs from radiographic images. Experiments show it outperforms traditional ORF in dealing with imbalanced data with a changing imbalance ratio, while maintaining a comparable accuracy and a higher efficiency compared with its offline counterpart. Our results demonstrate that DyBa ORF is more suitable than existing ORF for learning-based interactive image segmentation.

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“…1). By analogy to interactive segmentation algorithms, dedicated editing tools can modify the segmentation result in 2-D [14][15][16] or 3-D. [17][18][19][20][21][22][23][24] We have previously given a detailed overview on segmentation editing for medical imaging. 25 Note that even though an automatic segmentation algorithm might be used in the first stage, the segmentation process itself becomes interactive from the user's point of view as soon as an intervention, such as editing in whatever form, is required.…”

mentioning

confidence: 99%

On the evaluation of segmentation editing tools

et al. 2014

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Abstract. Efficient segmentation editing tools are important components in the segmentation process, as no automatic methods exist that always generate sufficient results. Evaluating segmentation editing algorithms is challenging, because their quality depends on the user's subjective impression. So far, no established methods for an objective, comprehensive evaluation of such tools exist and, particularly, intermediate segmentation results are not taken into account. We discuss the evaluation of editing algorithms in the context of tumor segmentation in computed tomography. We propose a rating scheme to qualitatively measure the accuracy and efficiency of editing tools in user studies. In order to objectively summarize the overall quality, we propose two scores based on the subjective rating and the quantified segmentation quality over time. Finally, a simulation-based evaluation approach is discussed, which allows a more reproducible evaluation without the need for human input. This automated evaluation complements user studies, allowing a more convincing evaluation, particularly during development, where frequent user studies are not possible. The proposed methods have been used to evaluate two dedicated editing algorithms on 131 representative tumor segmentations. We show how the comparison of editing algorithms benefits from the proposed methods. Our results also show the correlation of the suggested quality score with the qualitative ratings.

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IntellEditS: Intelligent Learning-Based Editor of Segmentations

Cited by 7 publications

References 16 publications

User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation

User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation

Dynamically Balanced Online Random Forests for Interactive Scribble-Based Segmentation

On the evaluation of segmentation editing tools

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