Unbiased Shape Compactness for Segmentation

Dolz, José; Ayed, Ismail Ben; Desrosiers, Christian

doi:10.1007/978-3-319-66182-7_86

Cited by 10 publications

(11 citation statements)

References 20 publications

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“…In these works, length is typically employed as a regularizer in the energy function. More complex regularizers have been demonstrated to further boost the performance of segmentation techniques, for example, convexity or compactness . Employing such regularizers may improve performance in the current application given the compact shape of the bladder.…”

Section: Discussionmentioning

confidence: 99%

“…More complex regularizers have been demonstrated to further boost the performance of segmentation techniques, for example, convexity 65 or compactness. 66 Employing such regularizers may improve performance in the current application given the compact shape of the bladder. Furthermore, recent works have shown that combining several deep models can lead to important improvements in several segmentation tasks.…”

Section: Discussionmentioning

confidence: 99%

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Multiregion segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks

Dolz

Rony

et al. 2018

Medical Physics

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Purpose Precise segmentation of bladder walls and tumor regions is an essential step toward noninvasive identification of tumor stage and grade, which is critical for treatment decision and prognosis of patients with bladder cancer (BC). However, the automatic delineation of bladder walls and tumor in magnetic resonance images (MRI) is a challenging task, due to important bladder shape variations, strong intensity inhomogeneity in urine, and very high variability across the population, particularly on tumors’ appearance. To tackle these issues, we propose to leverage the representation capacity of deep fully convolutional neural networks. Methods The proposed network includes dilated convolutions to increase the receptive field without incurring extra cost or degrading its performance. Furthermore, we introduce progressive dilations in each convolutional block, thereby enabling extensive receptive fields without the need for large dilation rates. The proposed network is evaluated on 3.0T T2‐weighted MRI scans from 60 pathologically confirmed patients with BC. Results Experiments show the proposed model to achieve a higher level of accuracy than state‐of‐the‐art methods, with a mean Dice similarity coefficient of 0.98, 0.84, and 0.69 for inner wall, outer wall, and tumor region segmentation, respectively. These results represent a strong agreement with reference contours and an increase in performance compared to existing methods. In addition, inference times are less than a second for a whole three‐dimensional (3D) volume, which is between two and three orders of magnitude faster than related state‐of‐the‐art methods for this application. Conclusion We showed that a CNN can yield precise segmentation of bladder walls and tumors in BC patients on MRI. The whole segmentation process is fully automatic and yields results similar to the reference standard, demonstrating the viability of deep learning models for the automatic multiregion segmentation of bladder cancer MRI images.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Multiregion segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks

Dolz

Rony

et al. 2018

Medical Physics

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“…Additionally, and following recent works that interpret the output of the CNN as unary potentials in an energy minimization problem, we plan to further investigate the impact of different regularization terms in the esophagus segmentation performance. For example, a compactness term was recently proposed in, showing significant improvements of CNN segmentation results.…”

Section: Discussionmentioning

confidence: 99%

Esophagus segmentation in CT via 3D fully convolutional neural network and random walk

et al. 2017

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Purpose: Precise delineation of organs at risk is a crucial task in radiotherapy treatment planning for delivering high doses to the tumor while sparing healthy tissues. In recent years, automated segmentation methods have shown an increasingly high performance for the delineation of various anatomical structures. However, this task remains challenging for organs like the esophagus, which have a versatile shape and poor contrast to neighboring tissues. For human experts, segmenting the esophagus from CT images is a time-consuming and error-prone process. To tackle these issues, we propose a random walker approach driven by a 3D fully convolutional neural network (CNN) to automatically segment the esophagus from CT images. Methods: First, a soft probability map is generated by the CNN. Then, an active contour model (ACM) is fitted to the CNN soft probability map to get a first estimation of the esophagus location. The outputs of the CNN and ACM are then used in conjunction with a probability model based on CT Hounsfield (HU) values to drive the random walker. Training and evaluation were done on 50 CTs from two different datasets, with clinically used peer-reviewed esophagus contours. Results were assessed regarding spatial overlap and shape similarity. Results: The esophagus contours generated by the proposed algorithm showed a mean Dice coefficient of 0.76 AE 0.11, an average symmetric square distance of 1.36 AE 0.90 mm, and an average Hausdorff distance of 11.68 AE 6.80, compared to the reference contours. These results translate to a very good agreement with reference contours and an increase in accuracy compared to existing methods. Furthermore, when considering the results reported in the literature for the publicly available Synapse dataset, our method outperformed all existing approaches, which suggests that the proposed method represents the current state-of-the-art for automatic esophagus segmentation. Conclusion: We show that a CNN can yield accurate estimations of esophagus location, and that the results of this model can be refined by a random walk step taking pixel intensities and neighborhood relationships into account. One of the main advantages of our network over previous methods is that it performs 3D convolutions, thus fully exploiting the 3D spatial context and performing an efficient volume-wise prediction. The whole segmentation process is fully automatic and yields esophagus delineations in very good agreement with the gold standard, showing that it can compete with previously published methods.

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“…Despite our intuitive analysis with regards to some relationships between loss performance and dataset characteristics however, we admit to many limitations. For starters, the proposed benchmark can not be generic, as there are many existent prior-based losses that we fail to include: low-level prior 16,19,32 , high-level topological 33 or shape prior 17,20 . Moreover, due to the fact that high-level prior-based losses are customized to target a particular property, providing means of comparison with respect to their effectiveness is subjected to debate.…”

Section: Limitations Of the Current Proposed Benchmarkmentioning

confidence: 99%

“…18 Prior could also be high-level representing actual external medical information such as the shape of the organ, compactness or size, and are optimized directly based on ground-truth prior tags. 12,17,20 Over the past few years, prior-based losses, whether low-level or high-level, present a rising trend in today's research in semantic image segmentation, particularly in the medical field. Given the diversity of prior-based losses on different medical imaging challenges and tasks, it has become hard to identify what loss works best for which dataset.…”

Section: Introductionmentioning

confidence: 99%

Effect of Prior-based Losses on Segmentation Performance: A Benchmark

Jurdi¹,

Petitjean²,

Cheplygina³

et al. 2022

Preprint

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Today, deep convolutional neural networks (CNNs) have demonstrated state-of-the-art performance for medical image segmentation, on various imaging modalities and tasks. Despite early success, segmentation networks may still generate anatomically aberrant segmentations, with holes or inaccuracies near the object boundaries. To enforce anatomical plausibility, recent research studies have focused on incorporating expert knowledge also known as prior knowledge, such as object shapes or boundary, as constraints in the loss function. Prior integrated could be low-level referring to reformulated representations extracted from the ground-truth segmentations, or high-level representing external medical information such as the organ's shape or size. Over the past few years, prior-based losses exhibited a rising interest in the research field since they allow integration of expert knowledge while still being architecture-agnostic. However, given the diversity of prior-based losses on different medical imaging challenges and tasks, it has become hard to identify what loss works best for which dataset. In this paper, we establish a benchmark of recent prior-based losses for medical image segmentation. The main objective is to provide intuition onto which losses to choose given a particular task or dataset, based on dataset characteristics and properties. To this end, four low-level and high-level prior-based losses are selected. The considered losses are validated on 8 different datasets from a variety of medical image segmentation challenges including the Decathlon, the ISLES and the WMH challenge. The proposed benchmark is conducted via a unified segmentation network and learning environment. The considered prior-based losses are varied in conjunction with the Dice loss across the different datasets. Results show that whereas low level prior-based losses can guarantee an increase in performance over the Dice loss baseline regardless of the dataset characteristics, high-level prior-based losses can increase anatomical plausibility as per data characteristic.

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Unbiased Shape Compactness for Segmentation

Cited by 10 publications

References 20 publications

Multiregion segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks

Multiregion segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks

Esophagus segmentation in CT via 3D fully convolutional neural network and random walk

Effect of Prior-based Losses on Segmentation Performance: A Benchmark

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