EScAPe: Eliminate Semantic Ambiguity in Prostate MRI Segmentation in Benign Prostatic Hyperplasia

Benign prostatic hyperplasia (BPH) is a prostate lesion, which is widely prevalent among middle-aged and elderly male individuals. Accurate segmentation of prostate structures in MRI imaging plays a critical role in aiding clinicians to assess the condition of the prostate gland. However, the boundary of the prostate gland often exhibits semantic features similar to those of adjacent connective tissues, leading to semantic confusion during prostate MRI segmentation in current methodologies. This phenomenon makes semantic ambiguity, which compromises clinicians' judgment of glandular conditions and may result in misdiagnosis of the patient's disease progression. This challenge imposes significant demands on existing prostate MRI segmentation models. Current models based on CNNs or Transformer architectures struggle to effectively capture long-range dependencies, rendering them prone to semantic confusion when processing images with indistinct boundaries. This limitation further exacerbates semantic ambiguities in the prostate MRI segmentation results. To mitigate the semantic ambiguity in prostate MRI segmentation in BPH, we propose the EScAPe, it contains Semantic Link Gates and a Cross-Layer Retro Connection. The Semantic Link Gate explicitly extracts semantic features at each point, mitigating information loss and semantic ambiguity. The Cross-Layer Retro Connection connects the initial and final layers, enabling the output to revisit the original features and preventing the loss of critical local features during sampling. We evaluate EScAPe on the MSD, NCI-ISBI 2013 and PICAI datasets compare its performance with existing Sota methods, and it achieved IoU scores of 61.66±4.99%, 80.18±%5.22, and 70.26±2.43% and Dice scores of 70.90±5.08%, 85.40±5.50%, and 81.15±2.18% on these three datasets respectively. Across most evaluation metrics, experiment shows that the EScAPe we proposed can solve the above-mentioned semantic ambiguity problem.