Ashley Ross

Cuiling Sun, Linkai Peng, Adam Murphy et al.

Automated 3D segmentation of prostate lesions from biparametric MRI (bp-MRI) is essential for reliable algorithmic analysis, but achieving high precision remains challenging. Volumetric methods must combine multiple modalities while ensuring anatomical consistency, but current models struggle to integrate cross-modal information reliably. While vision-language models (VLMs) are replacing the currently used architectural designs, they still lack the fine-grained, lesion-level semantics required for effective localized guidance. To address these limitations, we propose a new multi-encoder U-Net architecture incorporating three key innovations: (1) an alignment loss that enhances foreground text-image similarity to inject lesion semantics; (2) a heatmap loss that calibrates the similarity map and suppresses spurious background activations; and (3) a final-stage, confidence-gated multi-head cross-attention refiner that performs localized boundary edits in high-confidence regions. A phase-scheduled training regime stabilizes the optimization of these components. Our method consistently outperforms prior approaches, establishing a new state-of-the-art on the PI-CAI dataset through enhanced multi-modal fusion and localized text guidance. Our code is available at https://github.com/NUBagciLab/Prostate-Lesion-Segmentation.

Xin Zhu, Hongyi Pan, Yury Velichko et al.

Magnetic field inhomogeneity correction remains a challenging task in MRI analysis. Most established techniques are designed for brain MRI by supposing that image intensities in the identical tissue follow a uniform distribution. Such an assumption cannot be easily applied to other organs, especially those that are small in size and heterogeneous in texture (large variations in intensity), such as the prostate. To address this problem, this paper proposes a probabilistic Hadamard U-Net (PHU-Net) for prostate MRI bias field correction. First, a novel Hadamard U-Net (HU-Net) is introduced to extract the low-frequency scalar field, multiplied by the original input to obtain the prototypical corrected image. HU-Net converts the input image from the time domain into the frequency domain via Hadamard transform. In the frequency domain, high-frequency components are eliminated using the trainable filter (scaling layer), hard-thresholding layer, and sparsity penalty. Next, a conditional variational autoencoder is used to encode possible bias field-corrected variants into a low-dimensional latent space. Random samples drawn from latent space are then incorporated with a prototypical corrected image to generate multiple plausible images. Experimental results demonstrate the effectiveness of PHU-Net in correcting bias-field in prostate MRI with a fast inference speed. It has also been shown that prostate MRI segmentation accuracy improves with the high-quality corrected images from PHU-Net. The code will be available in the final version of this manuscript.

Ramin Nateghi, Ruoji Zhou, Madeline Saft et al.

Artificial intelligence may assist healthcare systems in meeting increasing demand for pathology services while maintaining diagnostic quality and reducing turnaround time and costs. We aimed to investigate the performance of an institutionally developed system for prostate cancer detection, grading, and workflow optimization and to contrast this with commercial alternatives. From August 2021 to March 2023, we scanned 21,396 slides from 1,147 patients receiving prostate biopsy. We developed models for cancer detection, grading, and screening of equivocal cases for IHC ordering. We compared the performance of task-specific prostate models with general-purpose foundation models in a prospectively collected dataset that reflects our patient population. We also evaluated the contributions of a bespoke model designed to improve sensitivity to small cancer foci and perception of low-resolution patterns. We found high concordance with pathologist ground-truth in detection (area under curve 98.5%, sensitivity 95.0%, and specificity 97.8%), ISUP grading (Cohen's kappa 0.869), grade group 3 or higher classification (area under curve 97.5%, sensitivity 94.9%, specificity 96.6%). Screening models could correctly classify 55% of biopsy blocks where immunohistochemistry was ordered with a 1.4% error rate. No statistically significant differences were observed between task-specific and foundation models in cancer detection, although the task-specific model is significantly smaller and faster. Institutions like academic medical centers that have high scanning volumes and report abstraction capabilities can develop highly accurate computational pathology models for internal use. These models have the potential to aid in quality control role and to improve resource allocation and workflow in the pathology lab to help meet future challenges in prostate cancer diagnosis.