The segmentation of cardiac MR images requires extensive attention as it needs a high level of care and analysis for the diagnosis of affected part. The advent of deep learning technology has paved the way for efficient, automatic and reliable segmentation of medical images for proper diagnosis. This manuscript proposes an augmented end to end trainable deep learning architecture for efficient segmentation of various domains of medical images. The model incorporates the depth wise separable convolution and group normalization as basic building blocks. Moreover, a noise stifler block is also induced between the encoder and decoder to counter the noise in the medical images. This path helps in delineating the precise boundary contours as the noise often reduces the boundary segmentation capability of the segmentation network. The network trained once produces exceedingly good results for the images of other datasets. An improvement of above (5 ± 0.03) % and (3.5 ± 0.02) % was observed in the Jaccard index and Dice score for cardiac MR images. The results are statistically validated as p < 0.05. The automatic computer investigated approach can help in reducing the burden on the medical system by producing accurate and reliable results. The algorithmic results were clinically verified by the senior radiologists by comparison with the manually segmented images. The training time of the network was about 30% less than U-net.

心脏 MR 图像的分割需要广泛的关注，因为它需要高水平的护理和分析来诊断受影响的部分。深度学习技术的出现为有效、自动和可靠地分割医学图像以进行正确诊断铺平了道路。该手稿提出了一种增强的端到端可训练深度学习架构，用于有效分割医学图像的各个领域。该模型将深度可分离卷积和组归一化作为基本构建块。此外，还在编码器和解码器之间引入了噪声抑制器块，以抵消医学图像中的噪声。该路径有助于描绘精确的边界轮廓，因为噪声通常会降低分割网络的边界分割能力。训练一次的网络对其他数据集的图像产生了非常好的结果。在心脏 MR 图像的 Jaccard 指数和 Dice 评分中观察到高于 (5 ± 0.03) % 和 (3.5 ± 0.02) % 的改善。结果经统计验证为p  < 0.05。自动计算机调查方法可以通过产生准确可靠的结果来帮助减轻医疗系统的负担。算法结果由资深放射科医师通过与人工分割图像的比较进行临床验证。该网络的训练时间比 U-net 少 30% 左右。