# A new smart-cropping pipeline for prostate segmentation using deep learning networks

arXiv: https://arxiv.org/abs/2107.02476 · HTML: https://mlsanity.com/paper/2107.02476/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 53 references that are in the map.
- First posted on arXiv 6 Jul 2021.

## Builds on (most cited first, 27 of 27)

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- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
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- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
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- [Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removal](https://mlsanity.com/paper/1808.10848/) (2018)
- [Optimization for Medical Image Segmentation: Theory and Practice when evaluating with Dice Score or Jaccard Index](https://mlsanity.com/paper/2010.13499/) (2020)
- [Recent Advances in Deep Learning for Object Detection](https://mlsanity.com/paper/1908.03673/) (2019)
- [Is the deconvolution layer the same as a convolutional layer?](https://mlsanity.com/paper/1609.07009/) (2016)
- [A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer](https://mlsanity.com/paper/1804.03280/) (2018)
- [Prostate Segmentation using 2D Bridged U-net](https://mlsanity.com/paper/1807.04459/) (2018)

## Abstract (arXiv)

Prostate segmentation from magnetic resonance imaging (MRI) is a challenging task. In recent years, several network architectures have been proposed to automate this process and alleviate the burden of manual annotation. Although the performance of these models has achieved promising results, there is still room for improvement before these models can be used safely and effectively in clinical practice. One of the major challenges in prostate MR image segmentation is the presence of class imbalance in the image labels where the background pixels dominate over the prostate. In the present work we propose a DL-based pipeline for cropping the region around the prostate from MRI images to produce a more balanced distribution of the foreground pixels (prostate) and the background pixels and improve segmentation accuracy. The effect of DL-cropping for improving the segmentation performance compared to standard center-cropping is assessed using five popular DL networks for prostate segmentation, namely U-net, U-net+, Res Unet++, Bridge U-net and Dense U-net. The proposed smart-cropping outperformed the standard center cropping in terms of segmentation accuracy for all the evaluated prostate segmentation networks. In terms of Dice score, the highest improvement was achieved for the U-net+ and ResU-net++ architectures corresponding to 8.9% and 8%, respectively.