LensePro: label noise-tolerant prototype-based network for improving cancer detection in prostate ultrasound with limited annotations.

Purpose: The standard of care for prostate cancer (PCa) diagnosis is the histopathological analysis of tissue samples obtained via transrectal ultrasound (TRUS) guided biopsy. Models built with deep neural networks (DNNs) hold the potential for direct PCa detection from TRUS, which allows targeted biopsy and subsequently enhances outcomes. Yet, there are ongoing challenges with training robust models, stemming from issues such as noisy labels, out-of-distribution (OOD) data, and limited labeled data.

Training deep neural networks with noisy clinical labels: toward accurate detection of prostate cancer in US data.

Purpose: Ultrasound is the standard-of-care to guide the systematic biopsy of the prostate. During the biopsy procedure, up to 12 biopsy cores are randomly sampled from six zones within the prostate, where the histopathology of those cores is used to determine the presence and grade of the cancer. Histopathology reports only provide statistical information on the presence of cancer and do not normally contain fine-grain information of cancer distribution within each core.

Coarse label refinement for improving prostate cancer detection in ultrasound imaging.

Purpose: Ultrasound-guided biopsy plays a major role in prostate cancer (PCa) detection, yet is limited by a high rate of false negatives and low diagnostic yield of the current systematic, non-targeted approaches. Developing machine learning models for accurately identifying cancerous tissue in ultrasound would help sample tissues from regions with higher cancer likelihood. A plausible approach for this purpose is to use individual ultrasound signals corresponding to a core as inputs and consider the histopathology diagnosis for the entire core as labels.

Towards targeted ultrasound-guided prostate biopsy by incorporating model and label uncertainty in cancer detection.

Purpose: Systematic prostate biopsy is widely used for cancer diagnosis. The procedure is blind to underlying prostate tissue micro-structure; hence, it can lead to a high rate of false negatives. Development of a machine-learning model that can reliably identify suspicious cancer regions is highly desirable.

Multiple instance learning combined with label invariant synthetic data for guiding systematic prostate biopsy: a feasibility study.

Purpose: Ultrasound imaging is routinely used in prostate biopsy, which involves obtaining prostate tissue samples using a systematic, yet, non-targeted approach. This approach is blinded to individual patient intraprostatic pathology, and unfortunately, has a high rate of false negatives.

Methods: In this paper, we propose a deep network for improved detection of prostate cancer in systematic biopsy.

Toward a real-time system for temporal enhanced ultrasound-guided prostate biopsy.

Purpose: We have previously proposed temporal enhanced ultrasound (TeUS) as a new paradigm for tissue characterization. TeUS is based on analyzing a sequence of ultrasound data with deep learning and has been demonstrated to be successful for detection of cancer in ultrasound-guided prostate biopsy. Our aim is to enable the dissemination of this technology to the community for large-scale clinical validation.

Investigation of Physical Phenomena Underlying Temporal-Enhanced Ultrasound as a New Diagnostic Imaging Technique: Theory and Simulations.

Temporal-enhanced ultrasound (TeUS) is a novel noninvasive imaging paradigm that captures information from a temporal sequence of backscattered US radio frequency data obtained from a fixed tissue location. This technology has been shown to be effective for classification of various in vivo and ex vivo tissue types including prostate cancer from benign tissue. Our previous studies have indicated two primary phenomena that influence TeUS: 1) changes in tissue temperature due to acoustic absorption and 2) micro vibrations of tissue due to physiological vibration.

Ultrasound-Guided Spine Anesthesia: Feasibility Study of a Guidance System.

Spinal needle injections are guided by fluoroscopy or palpation, resulting in radiation exposure and/or multiple needle re-insertions. Consequently, guiding these procedures with live ultrasound has become more popular, but images are still challenging to interpret. We introduce a guidance system based on augmentation of ultrasound images with a patient-specific 3-D surface model of the lumbar spine.

Ultrasound-guided spinal injections: a feasibility study of a guidance system.

Purpose: Facet joint injections of analgesic agents are widely used to treat patients with lower back pain. The current standard-of-care for guiding the injection is fluoroscopy, which exposes the patient and physician to significant radiation. As an alternative, several ultrasound guidance systems have been proposed, but have not become the standard-of-care, mainly because of the difficulty in image interpretation by the anesthesiologist unfamiliar with the complex spinal sonography.

Ultrasound RF time series for classification of breast lesions.

This work reports the use of ultrasound radio frequency (RF) time series analysis as a method for ultrasound-based classification of malignant breast lesions. The RF time series method is versatile and requires only a few seconds of raw ultrasound data with no need for additional instrumentation. Using the RF time series features, and a machine learning framework, we have generated malignancy maps, from the estimated cancer likelihood, for decision support in biopsy recommendation.