Vol 20, No 1 (2024)
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Published online: 2023-10-17

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Convolutional neural networks in auto-segmentation of nasopharyngeal carcinoma tumor — a systematic review and meta-analysis

Maryam Zamanian1, Iraj Abedi1
Oncol Clin Pract 2024;20(1):27-39.

Abstract

Introduction. Segmentation is one of the main stages of the treatment planning system (TPS), especially in nasopharyngeal carcinoma (NPC), because it is very heterogeneous and penetrates the skull bone tissue. An automated method to reduce the workload and human error caused by the lack of expertise and perspective would be very helpful. This meta-analysis evaluated the ability of convolutional neural networks (CNNs) to plan auto-segmentation computed tomography (CT) and magnetic resonance imaging (MRI) modalities. 

Material and methods. Articles published in PubMed, Scholar, and Cochrane databases were examined. The risk of bias was evaluated by the QUADAS-2 tool. The dice similarity coefficient (DSC) as the effect size and standard error (SE) as the precision index were analyzed by random effects. To calculate the degree of heterogeneity and its agent, we used (I2 and τ2) and meta-regression analysis (p < 0.05). A funnel plot was used to check for publication bias. 

Results. In general, eight studies on CT and 12 on MRI modalities were selected from 3601 studies. The heterogeneity based on (I2 and τ2) and DSC values (with a 95% confidence interval) for CT and MRI modalities were 88.7% (τ2 = 0.011), 0.67 (0.62–0.72), and 81.42% (τ2 =0.01), 0.76 (0.72–0.80), respectively. 

Conclusions. CNNs’ ability to segment both CT and MRI modalities is at a medium level, and its improvement can make it more suitable for clinical use.

REVIEW ARTICLE

Oncology in Clinical Practice

DOI: 10.5603/OCP.2023.0040

Copyright © 2024 Via Medica

ISSN 2450–1654

e-ISSN 2450–6478

Convolutional neural networks in auto-segmentation of nasopharyngeal carcinoma tumor a systematic review and meta-analysis

Maryam ZamanianIraj Abedi
Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Address for correspondence:

Dr. Iraj Abedi

Department of Medical Physics,

School of Medicine, Isfahan University

of Medical Sciences

Hezar Jarib Street, Postal code:

8174673461, Isfahan, Iran

e-mail: I.abedi@med.mui.ac.ir

Received: 11.04.2023 Accepted: 06.09.2023 Early publication date: 17.10.2023

ABSTRACT

Introduction. Segmentation is one of the main stages of the treatment planning system (TPS), especially in nasopharyngeal carcinoma (NPC), because it is very heterogeneous and penetrates the skull bone tissue. An automated method to reduce the workload and human error caused by the lack of expertise and perspective would be very helpful. This meta-analysis evaluated the ability of convolutional neural networks (CNNs) to plan auto-segmentation computed tomography (CT) and magnetic resonance imaging (MRI) modalities.

Material and methods. Articles published in PubMed, Scholar, and Cochrane databases were examined. The risk of bias was evaluated by the QUADAS-2 tool. The dice similarity coefficient (DSC) as the effect size and standard error (SE) as the precision index were analyzed by random effects. To calculate the degree of heterogeneity and its agent, we used (I2 and τ2) and meta-regression analysis (p < 0.05). A funnel plot was used to check for publication bias.

Results. In general, eight studies on CT and 12 on MRI modalities were selected from 3601 studies. The heterogeneity based on (I2 and τ2) and DSC values (with a 95% confidence interval) for CT and MRI modalities were 88.7% (τ2 = 0.011), 0.67 (0.620.72), and 81.42% (τ2 =0.01), 0.76 (0.720.80), respectively.

Conclusions. CNNs’ ability to segment both CT and MRI modalities is at a medium level, and its improvement can make it more suitable for clinical use.

Keywords: convolutional neural network, computed tomography, magnetic resonance imaging, nasopharyngeal carcinoma, segmentation

Oncol Clin Pract 2024; 20, 1: 2739

Introduction

Nasopharyngeal carcinoma (NPC) is the most common type of otolaryngological cancer that grows on the walls of the nasopharyngeal cavity. It has a heterogeneous distribution in different geographical regions, with the highest prevalence observed in Southeast Asia and moderate prevalence in South Asia and North Africa [1]. As the tumor grows and its grade increases to T4, it gradually spreads to the skeletal structure of the skull, even to the intracranial area [2].

The location of the tumor in the head and neck region, surrounding vital organs, and high sensitivity to radiation are reasons for choosing radiotherapy as the best treatment method [3]. The most important step in the treatment planning system (TPS), which is performed by an experienced radiation oncology specialist, is contouring of the tumor tissue (PTV) and the organs at risk (OARs) before starting the treatment, which can involve two imaging modalities: computed tomography (CT), magnetic resonance imaging (MRI), or both of them [4].

Magnetic resonance imaging can provide the best soft tissue contrast for NPC, and it is a painless and non-invasive method that does not require ionizing radiation, which makes it possible to repeat it to take different sequences (such as T1W, T2W, and T1C). It can also show the shape and location of the lesion well [5]. Because of NPC location, its spread to bone tissues, and the advantage of CT images, including the quality of imaging with better contrast in bony areas and high speed, CT is the best choice. In addition, CT scans take less time than MRI and are cost-effective and available. Centers may use either of these two modalities according to the patient’s condition [6, 7].

Image segmentation is a time-consuming person- -dependent task that requires the rendering skill of the oncologist; therefore, its correct execution creates a large workload, and the smallest error in segmentation affects the treatment plan [8]. In addition, segmentation of NPC tumors is more difficult due to their greater diversity and heterogeneous intensity compared to other tumors. One of the other challenges and problems of NPC segmentation is its metamorphic form, and each stage of treatment may require re-segmentation. For this reason, an automatic and accurate method to implement segmentation would be of great help [2, 9].

Among alternative methods that have been tested in recent years is the use of artificial intelligence for the automatic and accurate implementation of all TPS parts in various tumors [10]. In recent studies, Convolutional Neural Networks (CNNs) are evaluated rapidly in image auto-segmentation [11–13]. Therefore, in this study, we decided to comprehensively analyze the available literature on CNN ability to automatically perform NPC tumor segmentation in CT and MRI modalities.

Material and methods

We launched a comprehensive and systematic search of reliable sources to learn whether CNNs have sufficient ability to perform accurate segmentation. The study was registered at the beginning of its conceptualization in PROSPERO, the international open-access Prospective Register of Systematic Reviews (CRD42022379228).

Search strategy

We searched electronic databases, including MEDLINE (through PubMed) and Cochrane Library. In addition, a Google Scholar search of gray literature and publications in the arXiv database was conducted. There were no limitations regarding study language. Considering that the investigation of CNNs does not have a long history and has been evaluated only in recent years, no time limit was set for the search (in the year 2022). The terms used for the search strategy included (“Nasopharyngeal carcinoma”) AND (“Segmentation” OR “U-Net” OR “U-Res-Net” OR “Res-UNet”) AND (“Computed tomography” OR “CT” OR “Magnetic resonance imaging” OR “MRI”). PubMed was searched using the restriction of placing the [Title/Abstract] fields in all terms, and no field restriction was placed in Scholar.

After searching, Endnote software was used to collect articles. First, duplicate articles were excluded from the study. The screening of the studies was carried out in three steps: title, abstract, and full text. The search and screening of articles were performed by two researchers. Our assessment overlapped in 95% of cases, and in the remaining cases, we resolved differences of opinion based on the eligibility criteria.

Study exclusion criteria

All the selected studies investigated the power of all CNNs in relation to the NPC tumor segment, and the examination of OAR segments was excluded from the study. In terms of the investigated indicators, studies that reported the dice similarity coefficient (DSC) index were included. Studies in which the size of the network training samples was under 15 and studies that combined positron emission tomography (PET) images with CT and MRI were excluded from this analysis. All study reviews, case reports, editorials, and letters were excluded from the study.

Data extraction

The results were classified into two subgroups: CT and MRI modalities. The data extracted from the studies included the name of the first author, country and year of publication, network architecture, sample size and classification for training, external validation and testing, tumor staging, epochs number, learning rate, batch size, type of datasets, network dimension, CT contrast type, MRI sequence, feature extraction software, and processor characterization.

Furthermore, the indices of network performance included the DSC index and Hausdorff distance (HD) extracted from the studies. Meta-analysis results were reported using the 2020-PRISMA criteria, and the study protocol was written accordingly (Supplementary Tab. S1).

Quality assessment (risk of bias)

The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to evaluate the quality of m eta-analyses and the risk of bias. This tool evaluates the quality of diagnostic studies and includes four key domains: (1) patient selection random sampling, (2) index test (assessment blinded for and independent of reference test), (3) reference standard (valid reference test, assessment independent of index test), and (4) flow and timing (sufficient time between index and reference, all data points included in the analysis). The set of questions for each domain had answers on three scores including “yes-(1) score”, “no-(0) score”, and “unclear-(0) score”. This step was implemented by two persons.

Statistical analysis

Stata software (version 17.0; College Station, TX 77845, US) was used to perform all statistical calculations. Excel software (Microsoft 2016) was used to extract primary information from the articles and perform some basic calculations. One of the most important indices for evaluating CNN segmentation results is the DSC index, which is used as effect size. The heterogeneity studies were calculated by a random effect model, I2, τ2, and a level higher than 0.7 (I2 > 0) was considered an indicator of heterogeneity. To predict and investigate the effect of a variable on the obvious change in the results, the regression method was used, and the existence of possible publication bias was evaluated using a funnel plot.

Results

Study selection

Among the 3625 studies that were obtained by searching PubMed, Scholar, and Cochrane databases, 20 studies met eligibility criteria. A flow diagram of the study selection process is shown in Figure 1.

Figure 1. PRISMA flow diagram for study selection; CT computed tomography; MRI magnetic resonance imaging
Study characteristics and quality assessment

The reviewed studies on both modalities were conducted in China in the years 20182022. Different CNNs included 2D2.5D-3D UNet [14–26], modified UNet [17], 3D Res-UNet [17, 27, 28], modified 3D Res-UNet [14, 17], a mix of 2D and 3D Res-UNet [29, 30], 3D VNet [15], 3D SI-UNet [18], 3D Nested UNet [14, 19, 31], 3D AttR2-UNet [14, 21, 31], 3D LW-UNet [32], and 3D DE-UNet [33].

Magnetic resonance imagining modality studies used hospital data [21–24, 26, 28, 29, 31–35], and CT studies often used the 2019 MICCAI StructSeg data [14, 15, 17, 19] and hospital data [16, 18, 27, 30]. Two articles were conference papers [22, 30], and one was from the arXiv database [26].

In studies where CT images were analyzed, types of considered images were included without contrast (CT), and with contrast (CE-CT). Also, MRI images were the collection of different sequences of T1-Weighted (T1W), T2-Weighted (T2W), T1-Contrast (T1C), and multi-sequence (MS). In most studies of both modalities, full details of the task were not given; however, the epoch size was between 40 and 600, batch size was 18, and the learning rate was 0.010.001.

Result of risk of bias evaluation

The quality of the articles in the CT and MRI modality groups was evaluated using the QUADAS-2 tool, as presented in Figure 2.

Figure 2. Quality assessment of the studies. Part (A) is related to the quality assessment of computed tomography (CT) studies and part (B) is related to magnetic resonance imaging (MRI) studies
Result of meta-analysis

The descriptive characteristics and some performance results of NPC segmentation studies on CT scan MRI modalities are listed in Tables 1 [15–19, 27, 30, 36] and 2 [21–24, 26, 28, 29, 31–33, 35, 37], respectively.

Table 1. Details of the studies on convolutional neural networks segmentation of computed tomography (CT) images

The first author (publication year and country)

Sample Size

Training

[number]

External. validation

Testing N

Dataset

Tumor staging

Image type

Architecture

Epoch

Batch size

Learning Rate

DSC

(mean)

HD

(mean)

Processor specifications

Li et al. [16]

(2019; China)

502

302

100

100

West China Hospital

T1T4

CT

2D UNet

40

NM

0.01

74

32.10

Dual. Intel Xeon
E5-2643 v4 (3.4 GHz) and dual. NVIDIA Tesla K40m graphics card

Xue et al. [18]

(2020; China)

150

120

15

15

Hospital. of USTC

T1T4

CT

3D UNet

3D SI-UNet

200

NM

0.0001

84

74

9.7

8.7

One Intel Xeon Processor E5-2695 CPU and an NVIDIA Tesla P100 GPU memory

Wang et al. [27]

(2020; China)

205

NM

NM

NM

NM

T1T4

CE-CT CT

3D Res –UNet

NM

1

0.03

73

4.96

One NVIDIA GeForce RTX 2080Ti with 11 GB GPU memory

Bai et al. [17]

(2021; China)

60

50

No

10

StructSeg 2019 Challenge

T1T4

CT

2D UNet

2D PUNet

3D UNet

3D PUNet

3D Res-UNet

3D PRes-UNet

NM

8

0.0005

57.01

60.59

59.71

59.8

58.97

62.88

8.12

6.75

14.52

11.94

7.41

6.07

One NVIDIA RTX 2080Ti GPU and 32 GB memory

Liu et al. [19]

(2021; China)

140

60

No

40

2019 MICCAI StructSeg+

Sichuan Provincial. Cancer Hospital

T1T4

CT

3D UNet

3D Nested-UNet

300

4

0.001

33.9

25.6

13.2

13.7

NM

Mei et al. [15]

(2021; China)

50

40

No

10

2019 MICCAI StructSeg

T1T4

CT

3D UNet

2.5 UNet

3D VNet

NM

16

0.0001

59.91

62.16

61.02

NM

Two NVIDIA GTX 1080 Ti GPU memory

Jin et al. [30]

(2021; China)

90

63

18

9

Sichuan Cancer Hospital.
& Institute

T1T4

CT

3D PUNet

3D ResSE-UNet

200

8

0.0001

75

79

8.59

7.64

NM

Yang et al. [36]

(2022; China)

257

205

No

52

2019 MICCAI StructSeg

T1T4

CE-CT CT

3D UNet

3D PRes-UNet

3D AttR2-UNet

3D Nested-UNet

120

2

0.01

73.67

74.49

73.54

73.87

6.32

5.06

6.74

5.17

One NVIDIA GeForce RTX 2080Ti with 11 GB GPU memory

Table 2. Details of the studies on convolutional neural networks segmentation of magnetic resonance imaging (MRI)

The first author (publication year and country)

Sample Size

Training [number]

External. validation

Testing N

Dataset

Tumor staging

MRI sequence

Archit-ecture

Epoch

Batch size

Learning Rate

DSC
(mean)

HD
(mean)

Processor specifications

He et al. [22]

(2018; China)

19

18

No

1

NM

T1T4

T1W

3D UNet

NM

NM

0.0001

74.8

NM

Ubuntu 14.04 with Tesla K80 at 3.6GHz and 11.18GB GPU memory

Wang et al. [23]

(2018; China)

15

11

No

4

West China Hospital

T1T4

T1W

3D UNet

NM

NM

NM

79

NM

NM

Chen et al. [26]

(2019; China)

149

NM

NM

NM

Shandong Cancer Hospital

T1T4

T1W T2W T1C

MS

2D UNet

3D UNet

100

8

0.001

57.97

64.33

84.66

21.02

NVIDIA Titan Xp GPU with 12GB GPU memory

Lin et al. [28]

(2019; China)

1021

715

103

203

Sun Yat-sen University Cancer

Center

T1T4

T1W T2W T1C

3D Res-UNet

NM

NM

NM

79

NM

NM

Ye et al. [33]

(2020; China)

44

NM

NM

NM

Panyu Central. Hospital

T1T4

T2W T1W

2D DE-UNet

200

1

0.0001

66.1

NM

NVIDIA Geforce GTX 1080 TI with 11 GB GPU memory

Guo et al. [24]

(2020; China)

120

96

14

10

NM

T1T4

MRI

3D UNet

500

1

0.0001

73.7

NM

NM

Wang et al. [29]

(2021; China)

45

NM

NM

NM

West China Hospital

T1T4

T1W T2W T1C

MS

2D+3D Res-UNet

50

5

0.05

89.6

5.07

NM

Wong et al. [37]

(2021; China)

201

136

No

65

NM

T1T4

T2W T1W

2D UNet

75

4

0.005

71

NM

NM

Cai et al. [21]

(2021; China)

251

241

No

10

Shanghai Cancer Center

T1T4

T1W T2W T1C

3D UNet

3D AttR2-UNet

600

NM

0.0001

81.1

81.5

NM

Two NVIDIA Geforce GTX 1080 Ti GPU memory

Qi et al. [35]

(2021; China)

130

NM

NM

NM

Shandong Cancer Hospital

T1T4

T1W T2W T1C

3D UNet

NM

NM

NM

88.2

NM

NM

Zhang et al. [31]

(2022; China)

93

73

10

10

NM

T1T4

T1W T1C

2D AttR2-UNet

2D Nested-UNet

2D SE-UNet

100

3

0.001

73.8

79

78.7

NM

NM

Liu et al. [32]

(2022; China)

92

72

10

10

NM

T1T4

T1W T1C

2D

LW-UNet

NM

1

NM

81.3

NM

NM

NPC CT scan segmentation evaluation

Meta-analysis results of NPC segmentation studies of CT scan modality are presented as a forest plot in Figure 3. The pooled DSC was 0.67 (CI 95%, 0.62 to 0.72; I2 = 88.07%, t2 = 0.011) (p = 0.00) for CT scan segmentation.

Figure 3. Forest plot of computed tomography (CT) modality segmentation studies. The pooled-dice similarity coefficient (DSC) value [calculated with the 95% confidence interval (CI) with range for each study is reported]. Studies are sorted by year, and all Network type values are indicated (Net: Network type).
NPC MRI scan segmentation

The meta-analyses result of NPC segmentation on the MRI modality showed that the pooled DSC was 0.76 (95% CI 0.72 to 0.80; I2= 81.42%) (p = 0.01), and its forest plot is presented in Figure 4.

Figure 4. Forest plot of magnetic resonance imagining modality segmentation studies. The pooled dice similarity coefficient (DSC) value (calculated with a 95% confidence interval) with range for each study is reported. Studies are sorted by year, and all Network type values are indicated (Net: Network type)
Subgroup analysis

The type of networks and their dimensions were evaluated in the following subgroups:

  • CT scan: based on the number of network types, subgroups were divided into 12 categories. The number of six Network types was reported without a meta-analysis evaluation (including one study). The DSC index for 2.5D UNet, 2D UNet, 3D UNet, 2D P-UNet, 3D P-UNet, 3D AttR2-UNet, 3D Nested UNet, 3D Res-UNet, 3D P-Res-UNet, 3D ResSE-UNet, 3D SI-UNet, and 3D VNet was 0.62 (0.49 to 0.76), 0.67 (95% CI 0.50 to 0.83; I2 = 84.52%), 0.62 (0.95% CI 0.46 to 0.79; I2 = 95.64%), 0.61 (95% CI 0.48 to 0.73), 0.68 (0.95% CI 0.53 to 0.83; I2 = 74.68%), 0.74 (0.68 to 0.79), 0.64 (0.95% CI 0.25 to 1.02; I2 = 41.30%), 0.67 (95% CI 0.53 to 0.81; I2 =74.63%), 0.70 (95% CI 0.59 to 0.81; I2 = 74.68%), 0.79 (0.70 to 0.88), 0.74 (0.67
    to 0.81), and 0.61(0.4
    8 to 0.75), respectively.

Furthermore, the pooled DSC values for Network dimensions including 2D, 2.5D, and 3D were 0.65 (95% CI 0.54 to 0.76; I2 = 75.96%), 0.62 (0.49 to 0.76), and 0.68 (95% CI 0.62 to 0.74; I2 = 89.20%), respectively;

  • MRI scan: in this modality, network types were divided into ten categories (10 network types) which nine categories were reported without a meta-analysis evaluation (including one study). The DSC index on 2D UNet, 3D UNet, 2D AttR2-UNet, 3D AttR2-UNet, 2D Nested-UNet, 2D SE-UNet, 2D+3D Res-UNet, 3D Res-UNet, 3D DE-UNet, and 3D LW-UNet was 0.64 (0.57 to 0.72), 0.76 (95% CI 0.68 to 0.84; I2 = 87.20%), 0.78 (0.70 to0.87), 0.81 (0.77 to 0.86), 0.79 (0.71 to 0.87), 0.79 (0.70 to 0.87), 0.79 (0.67 to 0.91), 0.79 (0.77 to 0.81), 0.66 (0.52 to 0.80), and 0.81 (0.73 to 0.89), respectively.

The pooled DSC analysis for the subgroups of Network Dimensions including 2D, 2D + 3D, and 3D achieved 0.75 (95% CI 0.68 to 0.82; I2 = 67.92%), 0.79 (0.67 to 0.91) and 0.77 (95% CI 0.71 to 0.82; I2 = 86.87%), respectively.

Evaluation of possible causes of heterogeneity

In regression evaluation, coefficients of variables caused heterogeneity for CT studies based on the training number, external validation, and epoch number, which were (0.00073, p = 0.014), (–0.13648, p = 0.008), (–0.00109, p = 0.041), respectively, and for MRI studies based on batch size (–0.02199, p = 0.010).

Publication bias

We used a funnel plot to evaluate the publication bias in the studies that evaluated CNNs in image segmentation of both CT and MRI modalities (Fig. 5).

Figure 5. Funnel plot on computed tomography (A) and magnetic resonance imagining (B) modalities for evaluation of publication bias. The dice similarity coefficient (DSC) index was calculated as the effect size [95% confidence interval (CI)]

Discussion

The automatic system for the segmentation of heterogeneous NPC tumors is very valuable because it reduces the workload and speeds up diagnosis and treatment. It is necessary to know how successful deep learning networks have been so far, thus the results of this study will be very helpful in decision-making. The DSC index value was selected as the effect size parameter, and a meta-analysis was performed along with SE.

Convolutional neural networks as a subgroup of deep learning were initially tested as 2D in 2018 for CT scans and then in 2019 for MRI. After introducing innovative 3D networks, more studies have been devoted to these networks (Tab. 1, 2). However, 3D networks require a higher volume of calculations and more complex hardware for processing [39]. Recently, the expansion of network layers to improve network performance has been considered. AttR2-UNet and Nested-UNet are examples of such networks [40, 41].

Overall, considering the classification of the DSC index into three levels: good (0.8 DSC1), medium (0.6 DSC < 0.8), and poor (0 DSC < 0.6)] [38], both MRI image (0.76) and CT image (0.67) segmentation networks achieved medium results, while MRI studies obtained better results than NPC CT image segmentation studies. However, due to the different characteristics of the networks and the heterogeneous distribution of the studies in the two categories, it is not possible to draw definitive conclusions in this regard. The included studies were performed in the past five years, which indicates that this field is very new and will gain more success with further investigations.

In addition, the pooled DSC of both CT and MRI modalities for different dimensions of networks (2D–2.5D3D), reported almost similar values (~0.02 difference). In detail, the highest value of the DSC index for CT and MRI modalities was observed in 3D-ResSe-UNet (0.79), AttR2-UNet, and LW-UNet (0.81), respectively.

The limitation of analysis based on the results of the evaluated networks was the difference in details and performance of networks, such as the used loss function and epoch number even in similar networks. In addition, there was heterogeneity regarding the training of the networks using CT (with and without contrast) and MRI in different sequences. Due to the dependence of deep learning on the dataset, the heterogeneous distribution of patients, and the small number of patients in some geographical areas, may have affected the results of studies. Almost half of the CT scan segmentation studies used the same dataset presented in the 2019 MICCAI StructSeg, which reduces the impact of data type on the results and makes their comparison more valid. What is characteristic of these studies is that external validation was not performed in more than half of both modalities. Overall, we were able to reduce the heterogeneity analysis of the dimensions and type of the network subgroups.

Notably, all eligible studies were conducted in China, and on the other hand, the highest prevalence of NPC cancer was reported in China (~80%) [42]. Probably, the number of appropriate datasets, compared to other countries, and prioritizing this cancer in research facilitated the implementation of studies.

Since it is not easy to determine the margin of small tumors in magnetic resonance (MR) images, it may affect the ability of the network [43]. Therefore, more empowerment of networks to segmentation of MR images should be given more attention in future studies. Due to the impossibility of using contrast agents for patients with renal failure and the possibility of long-term complications [44], using images with contrast is likely to be used less in the future, thus it is better to enable networks to use non-contrast images.

Conclusions

The medium capability level of CNNs was observed in both CT and MRI modalities, while this capability was better in MRI segmentation. By improving CNNs, their clinical application can be made more practical.

Article Information and Declarations

Ethics statement

This article does not involve any studies with human participants or animals performed by any of the authors.

Author contributions

I.A. and M.Z. were equally involved in the design, literature review, and analysis of the study.

Funding

None.

Acknowledgments

None.

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

Supplementary Table S1.

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Supplementary material

Table S1. PRISIMA 2020 checklist (based on: Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021; 372: n71)

Section
and topic

Item

#

Checklist item

Location where item is reported

TITLE

Title

1

Identify the report as a systematic review

Page 1

ABSTRACT

Abstract

2

See the PRISMA 2020 for abstracts checklist

Page 2, P1

INTRODUCTION

Rationale

3

Describe the rationale for the review in the context of existing knowledge

Page 3, P4, 5, 6

Objectives

4

Provide an explicit statement of the objective(s) or question(s) the review addresses

Page 3, P7

METHODS

Eligibility criteria

5

Specify the inclusion and exclusion criteria for the review and how studies were grouped for the syntheses

Page 4, P4

Information sources

6

Specify all databases, registers, websites, organizations, reference lists and other sources searched or consulted to identify studies. Specify the date when each source was last searched or consulted

Page 4, P2

Search strategy

7

Present the full search strategies for all databases, registers and websites, including any filters and limits used

Page 4, P1

Selection process

8

Specify the methods used to decide whether a study met the inclusion criteria of the review, including how many reviewers screened each record and each report retrieved, whether they worked independently, and if applicable, details of automation tools used in the process

N/R

Data collection process

9

Specify the methods used to collect data from reports, including how many reviewers collected data from each report, whether they worked independently, any processes for obtaining or confirming data from study investigators, and if applicable, details of automation tools used in the process

Page 4, P1

Data items

10a

List and define all outcomes for which data were sought. Specify whether all results that were compatible with each outcome domain in each study were sought (e.g. for all measures, time points, analyses), and if not, the methods used to decide which results to collect

Page 4, P6

10b

List and define all other variables for which data were sought (e.g. participant and intervention characteristics, funding sources). Describe any assumptions made about any missing or unclear information

Page 4, P6

Study risk of bias assessment

11

Specify the methods used to assess the risk of bias in the included studies, including details of the tool(s) used, how many reviewers assessed each study and whether they worked independently, and if applicable, details of automation tools used in the process

Page 4, P5

Effect measures

12

Specify for each outcome the effect measure(s) (e.g. risk ratio, mean difference) used in the synthesis or presentation of results

Page 4, P6

Synthesis
methods

13a

Describe the processes used to decide which studies were eligible for each synthesis (e.g. tabulating the study intervention characteristics and comparing against the planned groups for each synthesis (item #5)

Page 5, P1

13b

Describe any methods required to prepare the data for presentation or synthesis, such as handling of missing summary statistics, or data conversions

Page 5, P1

13c

Describe any methods used to tabulate or visually display results of individual studies and syntheses

Page 5, P1

13d

Describe any methods used to synthesize results and provide a rationale for the choice(s). If meta-analysis was performed, describe the model(s), method(s) to identify the presence and extent of statistical heterogeneity, and software package(s) used

Page 5, P1

13e

Describe any methods used to explore possible causes of heterogeneity among study results (e.g. subgroup analysis, meta-regression)

Page 5, P1

13f

Describe any sensitivity analyses conducted to assess the robustness of the synthesized results

Page 5, P1

Section
and Topic

Item

#

Checklist item

Location where item is reported

Reporting bias assessment

14

Describe any methods used to assess risk of bias due to missing results in a synthesis (arising from reporting biases)

Page 4, P5

Certainty assessment

15

Describe any methods used to assess certainty (or confidence) in the body of evidence for an outcome

Page 5, P1

RESULTS

Study selection

16a

Describe the results of the search and selection process, from the number of records identified in the search to the number of studies included in the review, ideally using a flow diagram

Page 6, P3

16b

Cite studies that might appear to meet the inclusion criteria, but which were excluded, and explain why they were excluded

Page 6, P4,

Fig 1

Study characteristics

17

Cite each included study and present its characteristics

Page 6, P2-6

Risk of bias in studies

18

Present assessments of risk of bias for each included study

Page 6, P1,

Fig 2

Results of individual studies

19

For all outcomes, present, for each study: (a) summary statistics for each group (where appropriate) and (b) an effect estimate and its precision (e.g. confidence/credible interval), ideally using structured tables or plots

Page 1013

Results of syntheses

20a

For each synthesis, briefly summarize the characteristics and risk of bias among contributing studies

Page 6, P1

20b

Present results of all statistical syntheses conducted. If meta-analysis was done, present for each the summary estimate and its precision (e.g. confidence/credible interval) and measures of statistical heterogeneity. If comparing groups, describe the direction of the effect

Page 1013

20c

Present results of all investigations of possible causes of heterogeneity among study results

Page 1013

20d

Present results of all sensitivity analyses conducted to assess the robustness of the synthesized results

Page 1013

Reporting biases

21

Present assessments of risk of bias due to missing results (arising from reporting biases) for each synthesis assessed

Page 6, 14

Certainty of evidence

22

Present assessments of certainty (or confidence) in the body of evidence for each outcome assessed

Page 1013

DISCUSSION

Discussion

23a

Provide a general interpretation of the results in the context of other evidence

Page 15, P6

23b

Discuss any limitations of the evidence included in the review

Page 16, P5

23c

Discuss any limitations of the review processes used

Page 16, P5

23d

Discuss implications of the results for practice, policy, and future research

Page 16, P3

OTHER INFORMATION

Registration and protocol

24a

Provide registration information for the review, including register name and registration number, or state that the review was not registered

Page 3, P8

24b

Indicate where the review protocol can be accessed, or state that a protocol was not prepared

Page 3, P8

24c

Describe and explain any amendments to information provided at registration or in the protocol

Page 3, P8

Support

25

Describe sources of financial or non-financial support for the review, and the role of the funders or sponsors in the review

Page 16, P6

Competing interests

26

Declare any competing interests of review authors

Page 16, P7

Availability of data, code, and other materials

27

Report which of the following are publicly available and where they can be found: template data collection forms; data extracted from included studies; data used for all analyses; analytic code; any other materials used in the review

N/R