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Evolutionary Deep Attention Convolutional Neural Networks for 2D and 3D Medical Image Segmentation

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Abstract

Developing a convolutional neural network (CNN) for medical image segmentation is a complex task, especially when dealing with the limited number of available labelled medical images and computational resources. This task can be even more difficult if the aim is to develop a deep network and using a complicated structure like attention blocks. Because of various types of noises, artefacts and diversity in medical images, using complicated network structures like attention mechanism to improve the accuracy of segmentation is inevitable. Therefore, it is necessary to develop techniques to address the above difficulties. Neuroevolution is the combination of evolutionary computation and neural networks to establish a network automatically. However, Neuroevolution is computationally expensive, specifically to create 3D networks. In this paper, an automatic, efficient, accurate, and robust technique is introduced to develop deep attention convolutional neural networks utilising Neuroevolution for both 2D and 3D medical image segmentation. The proposed evolutionary technique can find a very good combination of six attention modules to recover spatial information from downsampling section and transfer them to the upsampling section of a U-Net-based network—six different CT and MRI datasets are employed to evaluate the proposed model for both 2D and 3D image segmentation. The obtained results are compared to state-of-the-art manual and automatic models, while our proposed model outperformed all of them.

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Data Availability

Datasets are publicly available

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Authors and Affiliations

  1. University of New South Wales, Canberra, Australia

    Tahereh Hassanzadeh, Daryl Essam & Ruhul Sarker

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  1. Tahereh Hassanzadeh
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  2. Daryl Essam
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  3. Ruhul Sarker
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Correspondence to Tahereh Hassanzadeh.

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Appendices

Appendix Extra Evaluation

2D Versus 3D

In this section, the proposed evolutionary 2D attention model is compared versus the proposed evolutionary 3D attention model.

Time Comparison

In this work, one NVIDIA GPU was used for training the 2D model and two NVIDIA GPUs were used for training the proposed 3D model. Figure 5 illustrates the required training time for 2D and 3D models. For example, our proposed model needs to be trained for about 24 days to find a set of 3D networks for 3D heart segmentation; however, the 2D model needs less than four days for training. Figure 5 shows a considerable difference between 2D and 3D models regarding the required computation. However, our 3D evolutionary model is still feasible to be implemented using a limited number of GPUs.

Fig. 5
figure 5

Comparison of 2D model versus 3D model regarding the required training time

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Parameter Comparison

In this section, the best obtained 2D attention network, its corresponding converted 3D network, and the evolutionary 3D attention network are compared regarding the number of trainable parameters. As shown in Fig. 6, the obtained 2D networks use less than a million parameters; however, with converting the 2D operations to 3D, the number of parameters are increased about five to six times. The final evolutionary attention 3D networks utilised two to less than nine million parameters, which can be considered a relatively small network for 3D image segmentation.

Fig. 6
figure 6

Comparison of 2D model versus 3D model regarding the number of training parameters

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Structure Comparison

Table 9 provides the genotypes of the best-found networks regarding each dataset in 2D and 3D. As shown in Table 9, each network has its own structural and training parameters. Table 9 shows the diversity of the found networks and the input data’s effects on the final network. According to the paper’s approach, each of these genotypes can be converted to their corresponding networks or phenotypes. It needs to be noted that each network was evolved with its own training parameters. For example, the best-found 2D network for the Sliver dataset is trained using Rmsprop optimiser with a learning rate of 0.001; the batch size is 8, augmentation size is 32000, and the “he-uniform” is used for the weight initialisation.

Table 9 The chromosomes of the best founded 2D and 3D networks using six various datasets
Full size table

Attention Modules

A comparison of the best five 2D and 3D networks utilising attention modules is provided in this section.

Figure 7 represents the distribution of utilised attention modules in the five best 2D networks regarding each dataset. As shown in Fig. 7, in some cases like the Sliver datasets, the best five networks utilised all modules in their structures; however, in some of them such as Prostate dataset five different types of attention modules are used in the five best networks. A similar pattern can be seen in the 3D networks (see Fig. 8). As shown in Fig. 8, the best 3D attention networks of Liver dataset only used residual activation unit and squeeze and excitation.

Based on the input data, and the obtained DSCs during evolution, the best combination of attention modules was selected for each network, which is a very difficult task if we design a network manually.

Fig. 7
figure 7

Distribution of utilised attention modules in five best 2D networks. 1: Concatenation, 2: dense, 3: residual, 4: residual activation unit, 5: attention residual, 6: squeeze and excitation

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Fig. 8
figure 8

Distribution of utilised attention modules in five best 3D networks. 1: Concatenation, 2: dense, 3: residual, 4: residual activation unit, 5: attention residual, 6: squeeze and excitation

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Extra Evaluation

Cross-Validation

To show our proposed 3D attention model’s robustness and remove randomness in our experiments, fourfold cross-validation was applied on the Sliver dataset. The Sliver dataset is one of the smallest datasets that we utilised in this work. Therefore it can be a good candidate for cross-validation. Table 10 shows the number of volumes in train, test, and validation sets for fourfold cross-validation. The number of volumes is shown as \(N\times M\times X\times Y\), where N is the number of volumes, M is the number of slices in a volume, X is width, and M is height of slices in a volume.

Table 10 The number of volumes in train, test, and validation sets for fourfold cross-validation using Sliver dataset
Full size table

For each fold, evolution started with 30 population and continued up to nine generations using 15 population. During the evolution, the validation set is used for evaluation of the networks. In the end, the five best networks are selected, and the obtained DSCs using test sets are reported in Table 11.

Table 11 The obtained DSCs of the five best networks in each fold
Full size table

As can be seen from Table 11, the proposed evolutionary attention model could obtain high accuracy 3D networks for 3D medical image segmentation regarding each fold.

Effect of Attention Modules

Also, to show the effect of using attention modules to recover and transfer extracted feature maps, examples of extracted features before and after residual activation unit and attention residual module are presented in Fig. 9. As shown in Fig. 9, the first row shows two examples of Heart and Hippocampus images along with their corresponding ground-truth. Figure 9b indicates a number of input feature maps to the residual activation unit, and Fig. 9c represents the module’s output feature maps. As can be seen from Fig. 9, after using the attention modules, a part of information about region of interest (RoI) is recovered. Also, a similar pattern can be seen for a Hippocampus image.

Fig. 9
figure 9

Examples of input and output feature maps of residual activation unit and attention residual block for Heart and Hippocampus images

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Subjective Comparison

In this section, another example of the segmented images regarding each dataset is provided as a subjective comparison. The results are compared versus six previous works, including Converted 2D to 3D [20], 3D U-Net [9], ConvNet [52], 3D Dense U-Net, UNet attention [44] [31], and NAS U-Net [50] respectively. NAS U-Net [50] is an automatic reinforcement-based technique to develop a network. Also, Converted 2D to 3D [20] is an automatic evolutionary model to develop networks and the rest are manually designed networks. As shown in Fig. 10, our proposed model could predict the ROI with high accuracy; however, over or under segmentation can be seen in some of the previous work’s results. For example, ConvNet even could not predict the ROI from the Hippocampus image. Because these networks are developed for a specific application or dataset, the networks structure or their training parameters need to be tuned accordingly when changing the application or dataset. It needs to be noted that all the previous works are implemented and trained based on their source papers.

Fig. 10
figure 10

One sample 3D segmented volume from each dataset. The red contour is the ground truth, cyan is proposed 3D attention model, green is Converted 2D to 3D [20], blue is 3D U-Net [9], grey is UNet attention [44], yellow is ConvNet [52], orange is 3D Dense U-Net [31], and magenta is NAS U-Net [50]

Full size image

Example of Crossover and Mutation

To clarify the crossover and mutation in our proposed model, an example is provided in Fig. 11. As shown in Fig. 11, two chromosomes are selected as parents, applying one-point crossover, two new offspring generated. Besides, three random mutations happened in one of the children and one mutation to the other one. This procedure needs to be repeated to create a new generation. After training the proposed model to the stated generation, a number of best networks will be selected as the final best networks.

Fig. 11
figure 11

An example of crossover and mutation

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Hassanzadeh, T., Essam, D. & Sarker, R. Evolutionary Deep Attention Convolutional Neural Networks for 2D and 3D Medical Image Segmentation. J Digit Imaging 34, 1387–1404 (2021). https://doi.org/10.1007/s10278-021-00526-2

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