FISICO: Fast Image SegmentatIon COrrection

Valenzuela, Waldo; Ferguson, Stephen J.; Ignasiak, Dominika; Diserens, Gaëlle; Häni, Levin; Wiest, Roland; Vermathen, Peter; Boesch, Chris

doi:10.1371/journal.pone.0156035

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…For further statistical significance, these experiments shall be repeated with larger data sets with varying imaging quality. Additionally, surface and Dice metrics used in model evaluation do not always correlate with time savings in manual contouring process . This necessitates the design of new metrics that quantify segmentation errors by taking into account the cost of required user interaction to correct them.…”

Section: Discussionmentioning

confidence: 99%

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Evaluation of Deep Learning to Augment Image-Guided Radiotherapy for Head and Neck and Prostate Cancers

et al. 2020

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IMPORTANCE Personalized radiotherapy planning depends on high-quality delineation of target tumors and surrounding organs at risk (OARs). This process puts additional time burdens on oncologists and introduces variability among both experts and institutions. OBJECTIVE To explore clinically acceptable autocontouring solutions that can be integrated into existing workflows and used in different domains of radiotherapy. DESIGN, SETTING, AND PARTICIPANTS This quality improvement study used a multicenter imaging data set comprising 519 pelvic and 242 head and neck computed tomography (CT) scans from 8 distinct clinical sites and patients diagnosed either with prostate or head and neck cancer. The scans were acquired as part of treatment dose planning from patients who received intensitymodulated radiation therapy between October 2013 and February 2020. Fifteen different OARs were manually annotated by expert readers and radiation oncologists. The models were trained on a subset of the data set to automatically delineate OARs and evaluated on both internal and external data sets. Data analysis was conducted October 2019 to September 2020. MAIN OUTCOMES AND MEASURES The autocontouring solution was evaluated on external data sets, and its accuracy was quantified with volumetric agreement and surface distance measures. Models were benchmarked against expert annotations in an interobserver variability (IOV) study. Clinical utility was evaluated by measuring time spent on manual corrections and annotations from scratch. RESULTS A total of 519 participants' (519 [100%] men; 390 [75%] aged 62-75 years) pelvic CT images and 242 participants' (184 [76%] men; 194 [80%] aged 50-73 years) head and neck CT images were included. The models achieved levels of clinical accuracy within the bounds of expert IOV for 13 of 15 structures (eg, left femur, κ = 0.982; brainstem, κ = 0.806) and performed consistently well across both external and internal data sets (eg, mean [SD] Dice score for left femur, internal vs external data sets: 98.52% [0.50] vs 98.04% [1.02]; P = .04). The correction time of autogenerated contours on 10 head and neck and 10 prostate scans was measured as a mean of 4.98 (95% CI, 4.44-5.52) min/scan and 3.40 (95% CI, 1.60-5.20) min/scan, respectively, to ensure clinically accepted accuracy, whereas contouring from scratch on the same scans was observed to be 73.25 (95% CI, 68.68-77.82) min/scan and 86.75 (95% CI, 75.21-92.29) min/scan, respectively, accounting for a 93% reduction in time. CONCLUSIONS AND RELEVANCE In this study, the models achieved levels of clinical accuracy within expert IOV while reducing manual contouring time and performing consistently well across previously unseen heterogeneous data sets. With the availability of open-source libraries and reliable (continued) Key Points Question Can machine learning models achieve clinically acceptable accuracy in image segmentation tasks in radiotherapy planning and reduce overall contouring time? Findings This quality improvement study was conducted on a set o...

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Section: Discussionmentioning

confidence: 99%

“…Additionally, surface and Dice metrics used in model evaluation do not always correlate with time savings in manual contouring process. 15,34 This necessitates the design of new metrics that quantify segmentation errors by taking into account the cost of required user interaction to correct them.…”

Section: Limitationsmentioning

confidence: 99%

Evaluation of Deep Learning to Augment Image-Guided Radiotherapy for Head and Neck and Prostate Cancers

et al. 2020

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“…As for humanproofreading, there are already several publicly available medical image processing software with manual annotation main tools, such as ITK-SNAP [12], MITK [13], 3D Slicer [14], TurgleSeg [15] and Seg3D [16]. Most of them provided interaction functions including pixel painting, contour interpolation [13,15,17,18], interactive level sets [19], surface adjustment [20], super-pixel [21] and super-voxel [22] modification. However, these tools were developed for general segmentation purposes; none of them was dedicatedly designed for efficient correction of neural network outputs.…”

Section: Introductionmentioning

confidence: 99%

Efficient contour-based annotation by iterative deep learning for organ segmentation from volumetric medical images

et al. 2022

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Purpose Training deep neural networks usually require a large number of human-annotated data. For organ segmentation from volumetric medical images, human annotation is tedious and inefficient. To save human labour and to accelerate the training process, the strategy of annotation by iterative deep learning recently becomes popular in the research community. However, due to the lack of domain knowledge or efficient human-interaction tools, the current AID methods still suffer from long training time and high annotation burden. Methods We develop a contour-based annotation by iterative deep learning (AID) algorithm which uses boundary representation instead of voxel labels to incorporate high-level organ shape knowledge. We propose a contour segmentation network with a multi-scale feature extraction backbone to improve the boundary detection accuracy. We also developed a contour-based human-intervention method to facilitate easy adjustments of organ boundaries. By combining the contour-based segmentation network and the contour-adjustment intervention method, our algorithm achieves fast few-shot learning and efficient human proofreading. Results For validation, two human operators independently annotated four abdominal organs in computed tomography (CT) images using our method and two compared methods, i.e. a traditional contour-interpolation method and a state-of-the-art (SOTA) convolutional network (CNN) method based on voxel label representation. Compared to these methods, our approach considerably saved annotation time and reduced inter-rater variabilities. Our contour detection network also outperforms the SOTA nnU-Net in producing anatomically plausible organ shape with only a small training set. Conclusion Taking advantage of the boundary shape prior and the contour representation, our method is more efficient, more accurate and less prone to inter-operator variability than the SOTA AID methods for organ segmentation from volumetric medical images. The good shape learning ability and flexible boundary adjustment function make it suitable for fast annotation of organ structures with regular shape.

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