Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection

Pisov, Maxim; Goncharov, Mikhail; Курочкина, Н. С.; Morozov, S. P.; Gombolevskiy, Victor A.; Chernina, Valeria; Vladzymyrskyy, Anton; Zamyatina, Ksenia; Chesnokova, Anna; Pronin, Igor; Shifrin, Michael; Belyaev, Mikhail

doi:10.1007/978-3-030-33850-3_4

Cited by 21 publications

(28 citation statements)

References 16 publications

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“…Beyond linear models, other brain and neuro-systems can be modeled with relevant subject-content knowledge for better interpretability as well. Segmentation task for the detection of brain midline shift is performed using using CNN with standard structural knowledge incorporated [133]. A template called model-derived age norm is derived from mean values of sleep EEG features of healthy subjects [157].…”

Section: B Interpretability Via Mathematical Structure 1) Predefinedmentioning

confidence: 99%

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

Tjoa

Guan

2021

IEEE Trans. Neural Netw. Learning Syst.

1,084

547

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Recently, artificial intelligence and machine learning in general have demonstrated remarkable performances in many tasks, from image processing to natural language processing, especially with the advent of deep learning (DL). Along with research progress, they have encroached upon many different fields and disciplines. Some of them require high level of accountability and thus transparency, for example, the medical sector. Explanations for machine decisions and predictions are thus needed to justify their reliability. This requires greater interpretability, which often means we need to understand the mechanism underlying the algorithms. Unfortunately, the blackbox nature of the DL is still unresolved, and many machine decisions are still poorly understood. We provide a review on interpretabilities suggested by different research works and categorize them. The different categories show different dimensions in interpretability research, from approaches that provide "obviously" interpretable information to the studies of complex patterns. By applying the same categorization to interpretability in medical research, it is hoped that: 1) clinicians and practitioners can subsequently approach these methods with caution; 2) insight into interpretability will be born with more considerations for medical practices; and 3) initiatives to push forward data-based, mathematically grounded, and technically grounded medical education are encouraged.

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Section: B Interpretability Via Mathematical Structure 1) Predefinedmentioning

confidence: 99%

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

Tjoa

Guan

2021

IEEE Trans. Neural Netw. Learning Syst.

1,084

547

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“…This performance directly validates the robustness of our method in case of severe brain disease. For the regression results, the predictions of our method are slightly better than Maxim’s, 9 especially for the case in the second row.…”

Section: Resultsmentioning

confidence: 85%

“…For the first task, we consider

{\overset{false^}{y}}_{fuse}

as the results and compare them with five other leading methods on this field: HED, 12 SRN, 10 RCF, 20 HiFi, 21 and MSB‐FCN 13 . For the regression task,

{\overset{false^}{Y}}_{R}

is compared with Maxim's 9 and VGG‐16, 15 which is used as the baseline in our experiments. Due to the final goal of our task is to get the locations of midline, we introduce the following equation to convert the probability map from skeleton extraction‐based methods into coordinates

{\overset{false^}{Y}}_{midline}

to fairly compare with regression‐based methods:

{\overset{Y}{true}}_{italicmidline} (i) = {false\sum}_{j}^{W} j \cdot S e g ()(, i, j)

where

italicSeg

means the midline probability map produced by skeleton extraction methods and

S e g (i, j)

represents the value at the location of

()(, i, j)

.…”

Section: Resultsmentioning

confidence: 99%

“…Based on that, the semantic information covered overall brain can be introduced to obtain better performance. Maxim et al 9 adopted this idea and proposed a structural knowledge (segmentation information) based regression method to delineate midline. In this study, we take it one step closer and directly supervise this semantic information by formulating it as the skeleton extraction 10 task (also can be regarded as segmentation task), not just indirect adaptation.…”

Section: Introductionmentioning

confidence: 99%

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The delineation of largely deformed brain midline using regression‐based line detection network

et al. 2020

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Purpose The human brain has two cerebral hemispheres that are roughly symmetric and separated by a midline, which is nearly a straight line shown in axial computed tomography (CT) images in healthy subjects. However, brain diseases such as hematoma and tumors often cause midline shift, where the degree of shift can be regarded as a quantitative indication in clinical practice. To facilitate clinical evaluation, we need computer‐aided methods to automate this quantification. Nevertheless, most existing studies focused on the landmark‐ or symmetry‐based methods that provide only the existence of shift or its maximum distance, which could be easily affected by anatomical variability and large brain deformations. Intuitive results such as midline delineation or measurement are lacking. In this study, we focus on developing an automated and robust method based on the fully convolutional neural network for the delineation of midline in largely deformed brains. Methods We propose a novel regression‐based line detection network (RLDN) for the robust midline delineation, especially in largely deformed brains. Specifically, to improve the robustness of delineation in largely deformed brains, we regard the delineation of the midline as the skeleton extraction task and then use the multiscale bidirectional integration module to acquire more representative features. Based on the skeleton extraction, we incorporate the regression task into it to delineate more accurate and continuous midline, especially in largely deformed brains. Our study utilized the public CQ 500 dataset (128 subjects) for training with hold‐out validation on 61 subjects from a private cohort accrued from a local hospital. Results The mean line distance error and F1‐score were 1.17 ± 0.72 mm with 0.78 on CQ 500 test set, and 4.15 ± 3.97 mm with 0.61 on the private dataset. Besides, significant differences (P < 0.05) were observed between our method and other comparative ones on these two datasets. Conclusions This work provides a novel solution to acquire robust delineation of the midline, especially in largely deformed brains, and achieves state‐of‐the‐art performance on the public and our private dataset, which makes it possible for automated diagnosis of relevant brain diseases in the future.

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“…With the rise of deep learning in medical image segmentation, mid-sagittal surface estimation could potentially be improved upon through these techniques. A recent paper by Pisov et al ( 2019 ) uses convolutional neural networks (CNNs) for brain midline shift (MLS) detection. They introduced a novel deep learning based approach for MLS detection, which exploits task-specific structural knowledge.…”

Section: Discussionmentioning

confidence: 99%

Automated Midline Estimation for Symmetry Analysis of Cerebral Hemispheres in FLAIR MRI

Gibicar

Moody

Khademi

2021

Front. Aging Neurosci.

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To perform brain asymmetry studies in large neuroimaging archives, reliable and automatic detection of the interhemispheric fissure (IF) is needed to first extract the cerebral hemispheres. The detection of the IF is often referred to as mid-sagittal plane estimation, as this plane separates the two cerebral hemispheres. However, traditional planar estimation techniques fail when the IF presents a curvature caused by existing pathology or a natural phenomenon known as brain torque. As a result, midline estimates can be inaccurate. In this study, a fully unsupervised midline estimation technique is proposed that is comprised of three main stages: head angle correction, control point estimation and midline generation. The control points are estimated using a combination of intensity, texture, gradient, and symmetry-based features. As shown, the proposed method automatically adapts to IF curvature, is applied on a slice-to-slice basis for more accurate results and also provides accurate delineation of the midline in the septum pellucidum, which is a source of failure for traditional approaches. The method is compared to two state-of-the-art methods for midline estimation and is validated using 75 imaging volumes (~3,000 imaging slices) acquired from 38 centers of subjects with dementia and vascular disease. The proposed method yields the lowest average error across all metrics: Hausdorff distance (HD) was 0.32 ± 0.23, mean absolute difference (MAD) was 1.10 ± 0.38 mm and volume difference was 7.52 ± 5.40 and 5.35 ± 3.97 ml, for left and right hemispheres, respectively. Using the proposed method, the midline was extracted for 5,360 volumes (~275K images) from 83 centers worldwide, acquired by GE, Siemens and Philips scanners. An asymmetry index was proposed that automatically detected outlier segmentations (which were <1% of the total dataset). Using the extracted hemispheres, hemispheric asymmetry texture biomarkers of the normal-appearing brain matter (NABM) were analyzed in a dementia cohort, and significant differences in biomarker means were found across SCI and MCI and SCI and AD.

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Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection

Cited by 21 publications

References 16 publications

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

The delineation of largely deformed brain midline using regression‐based line detection network

Automated Midline Estimation for Symmetry Analysis of Cerebral Hemispheres in FLAIR MRI

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