Accurate and Robust Segmentation of the Clinical Target Volume for Prostate Brachytherapy

Karimi, Davood; Zeng, Qi; Mathur, Prateek; Avinash, Apeksha; Mahdavi, Sara; Spadinger, Ingrid; Abolmaesumi, Purang; Salcudean, Septimiu E.

doi:10.1007/978-3-030-00937-3_61

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…They segmented the prostate in TRUS images using a residual neural net and dilated convolution at deeper layers. Later on, Karimi et al (2018) approached the problem using CNN. In addition to segmenting the target using CNN with an adaptive sampling strategy to focus on images difficult to segment, they also trained an ensemble of models to estimate segmentation uncertainty.…”

Section: Segmentationmentioning

confidence: 99%

Emerging technologies in brachytherapy

Song¹,

Robar²,

Morén³

et al. 2021

Phys. Med. Biol.

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Brachytherapy is a mature treatment modality. The literature is abundant in terms of review articles and comprehensive books on the latest established as well as evolving clinical practices. The intent of this article is to part ways and look beyond the current state-of-the-art and review emerging technologies that are noteworthy and perhaps may drive the future innovations in the field. There are plenty of candidate topics that deserve a deeper look, of course, but with practical limits in this communicative platform, we explore four topics that perhaps is worthwhile to review in detail at this time. First, intensity modulated brachytherapy (IMBT) is reviewed. The IMBT takes advantage of anisotropic radiation profile generated through intelligent high-density shielding designs incorporated onto sources and applicators such to achieve high quality plans. Second, emerging applications of 3D printing (i.e. additive manufacturing) in brachytherapy are reviewed. With the advent of 3D printing, interest in this technology in brachytherapy has been immense and translation swift due to their potential to tailor applicators and treatments customizable to each individual patient. This is followed by, in third, innovations in treatment planning concerning catheter placement and dwell times where new modelling approaches, solution algorithms, and technological advances are reviewed. And, fourth and lastly, applications of a new machine learning technique, called deep learning, which has the potential to improve and automate all aspects of brachytherapy workflow, are reviewed. We do not expect that all ideas and innovations reviewed in this article will ultimately reach clinic but, nonetheless, this review provides a decent glimpse of what is to come. It would be exciting to monitor as IMBT, 3D printing, novel optimization algorithms, and deep learning technologies evolve over time and translate into pilot testing and sensibly phased clinical trials, and ultimately make a difference for cancer patients. Today’s fancy is tomorrow’s reality. The future is bright for brachytherapy.

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Section: Segmentationmentioning

confidence: 99%

Emerging technologies in brachytherapy

Song¹,

Robar²,

Morén³

et al. 2021

Phys. Med. Biol.

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“…To tackle the missing boundary issue in TRUS images, Yang et al [41] proposed to learn the shape prior with the biologically plausible recurrent neural networks (RNNs) and bridged boundary incompleteness. Karimi et al [42] employed an ensemble of multiple CNN models and a statistical shape model to segment TRUS images for prostate brachytherapy. Anas et al [43] employed a deep residual neural net with an exponential weight map to delineate the 2D TRUS images for low-dose prostate brachytherapy treatment.…”

Section: A Relevant Workmentioning

confidence: 99%

Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound

Wang,

Dou,

et al. 2019

Preprint

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Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of essential importance for imageguided prostate interventions and treatment planning. However, developing such automatic solutions remains very challenging due to the missing/ambiguous boundary and inhomogeneous intensity distribution of the prostate in TRUS, as well as the large variability in prostate shapes. This paper develops a novel 3D deep neural network equipped with attention modules for better prostate segmentation in TRUS by fully exploiting the complementary information encoded in different layers of the convolutional neural network (CNN). Our attention module utilizes the attention mechanism to selectively leverage the multilevel features integrated from different layers to refine the features at each individual layer, suppressing the non-prostate noise at shallow layers of the CNN and increasing more prostate details into features at deep layers. Experimental results on challenging 3D TRUS volumes show that our method attains satisfactory segmentation performance. The proposed attention mechanism is a general strategy to aggregate multi-level deep features and has the potential to be used for other medical image segmentation tasks. The code is publicly available at https://github.com/wulalago/DAF3D.

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“…To cope with these issues semi-automatic and automatic algorithms are highly expected by clinical teams. Several prostate segmentation approaches have been proposed over the past decades [3][4][5]. Nevertheless most of them deal with the segmentation of 3D MR images where the problem is considered a bit easier.…”

Section: Introductionmentioning

confidence: 99%

“…Several prostate segmentation approaches have been proposed over the past decades. [3][4][5] Nevertheless, most of them deal with the segmentation of 3D MR images where the problem is considered a bit easier. Indeed, on TRUS images, prostate boundaries are often weakly present or absent, especially at the base and apex, and various types of artifacts may be present.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐eXpert fusion: An ensemble learning framework to segment 3D TRUS prostate images

Beitone

Troccaz

2022

Medical Physics

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Purpose: Prostate segmentation of 3D TRUS images is a prerequisite for several diagnostic and therapeutic applications. Unfortunately, this difficult task suffers from high intra-and inter-observer variability, even for experienced urologists/radiologists. This is why automatic segmentation algorithms could have a significant clinical added-value. Methods: This paper introduces a new deep segmentation architecture consisting of two main phases: view-specific segmentations of 2D slices and their fusion. The segmentation phase is based on three segmentation networks trained in parallel on specific slice viewing directions: axial, coronal, sagittal. The proposed fusion network is then fed with the output of the segmentation networks and trained to produce three confidence maps. These maps correspond to the local trust granted by the fusion network to each view-specific segmentation network. Finally, for a given slice, the segmentation is computed by combining these confidence maps with their corresponding segmentations. The 3D segmentation of the prostate is obtained by re-stacking all the segmented slices to form a volume.Results: This approach was evaluated on a database of 100 patients with several combinations of network architectures (for both the segmentation phase and the fusion phase) to show the flexibility and reliability of the framework. The proposed approach was also compared to STAPLE, to the majority voting strategy and to a direct 3D approach tested on the same database. The new method outperforms these three approaches on all evaluation criteria. Finally, the results of the Multi-eXpert Fusion (MXF) framework compare favorably with other state-of-the-art methods while these methods typically work on smaller databases. Conclusions: We proposed a novel MXF framework to segment 3D TRUS images of the prostate. The main feature of this approach is the fusion of expert networks results at the pixel level using computed confidence maps. Experiments conducted on a clinical database have shown the robustness and flexibility of this approach and its superiority over state-of-the-art approaches. Finally, the MXF framework demonstrated its ability to capture and preserve the underlying gland structures, particularly in the base and apex regions.

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Accurate and Robust Segmentation of the Clinical Target Volume for Prostate Brachytherapy

Cited by 6 publications

References 7 publications

Emerging technologies in brachytherapy

Emerging technologies in brachytherapy

Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound

Multi‐eXpert fusion: An ensemble learning framework to segment 3D TRUS prostate images

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