A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Development and Validation of Mechatronic Systems for Image-Guided Needle Interventions and Point-of-Care Breast Cancer Screening with Ultrasound (2D and 3D) and Positron Emission Mammography

The successful intervention of breast cancer relies on effective early detection and definitive diagnosis. While conventional screening mammography has substantially reduced breast cancer-related mortalities, substantial challenges persist in women with dense breasts. Additionally, complex interrelated risk factors and healthcare disparities contribute to breast cancer-related inequities, which restrict accessibility, impose cost constraints, and reduce inclusivity to high-quality healthcare. These limitations predominantly stem from the inadequate sensitivity and clinical utility of currently available approaches in increased-risk populations, including those with dense breasts, underserved and vulnerable populations. This PhD dissertation aims to describe the development and validation of alternative, cost-effective, robust, and high-resolution systems for point-of-care (POC) breast cancer screening and image-guided needle interventions. Specifically, 2D and 3D ultrasound (US) and positron emission mammography (PEM) were employed to improve detection, independent of breast density, in conjunction with mechatronic and automated approaches for accurate image acquisition and precise interventional workflow. First, a mechatronic guidance system for US-guided biopsy under high-resolution PEM localization was developed to improve spatial sampling of early-stage breast cancers. Validation and phantom studies showed accurate needle positioning and 3D spatial sampling under simulated PEM localization. Subsequently, a whole-breast spatially-tracked 3DUS system for point-of-care screening was developed, optimized, and validated within a clinically-relevant workspace and healthy volunteer studies. To improve robust image acquisition and adaptability to diverse patient populations, an alternative, cost-effective, portable, and patient-dedicated 3D automated breast (AB) US system for point-of-care screening was developed. Validation showed accurate geometric reconstruction, feasible clinical workflow, and proof-of-concept utility across healthy volunteers and acquisition conditions. Lastly, an orthogonal acquisition and 3D complementary breast (CB) US generation approach were described and experimentally validated to improve spatial resolution uniformity by recovering poor out-of-plane resolution. These systems developed and described throughout this dissertation show promise as alternative, cost-effective, robust, and high-resolution approaches for improving early detection and definitive diagnosis. Consequently, these contributions may advance breast cancer-related equities and improve outcomes in increased-risk populations and limited-resource settings

Breast Mass Characterization Using 3‐Dimensional Automated Ultrasound as an Adjunct to Digital Breast Tomosynthesis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135628/1/jum201332193.pd

Automatizirani pregled dojke ultrazvukom

Due to the growing number of breast cancer patients, an early diagnosis is important in order to reduce the mortality rate of those affected. Methods such as mammography, DBT, MRI, HHUS or ABUS are used in the detection of breast cancer. The aim of this article is to review the literature showing the basic principle of ABUS and to point out its advantages and disadvantages in relation to conventional methods of breast imaging. ABUS is a relatively new ultrasound method that performs well on patients with dense breast tissue. It reduces operator dependence and provides valuable diagnostic information with multiplanar reconstructions. Using evidence from reliable researches, studies have demonstrated that ABUS has a higher diagnostic accuracy compared to mammography, which remains the primary modality for early diagnosis of breast cancer. Applying ABUS as an adjunct to mammography during the screening test has proven effective and further confirmed the importance of their application in clinical practice. The disadvantage of the combination of ABUS and mammography was that in a large number of studies the specificity was lower compared to mammography itself. Compared to DBT, ABUS has demonstrated to have a higher diagnostic performance, with the exception that it lacks the ability to effectively detect calcifications. Although MRI seem to outperform ABUS, ABUS devices offer a cost-effective and easy to use imaging system, making it the best alternative. The HHUS technique, on the other hand, was perceived by many studies as less painful, with a shorter operative time compared to ABUS. However, the sensitivity and specificity of this screening method continues to remain inferior to ABUS. The use of artificial intelligence is becoming widely used today. As a result, the CAD software has been developed to be applied in conjunction with ABUS in order to improve the detection rate of breast cancer as well as its accuracy. The use of CAD significantly reduced image reading time and improved the overall diagnostic accuracy of ABUS. According to all the presented data, the use of ABUS medical devices in clinical practice continues to grow in importance and with the further development of technology and medicine, its full integration into healthcare systems around the world is expected.Zbog sve većeg broja oboljelih od raka dojke, a kako bi se smanjila smrtnost, vrlo je važna rana dijagnostika. U dijagnostici raka dojke koriste se metode kao što su mamografija, DBT, MRI, HHUS ili ABUS. Cilj ovoga rada je bio pregledom literature prikazati princip rada ABUS-a te ukazati na njegove prednosti i nedostatke u odnosu na konvencionalne metode snimanja dojki. ABUS je relativno nova ultrazvučna metoda koja je pokazala izvrsne rezultate kod žena s gustim grudima. Korištenje ABUS-a smanjuje ovisnost o operateru, a omogućuje vrijedne dijagnostičke informacije s multiplanarnim rekonstrukcijama. Pregledom brojnih istraživanja u ovom radu, ABUS se pokazao kao značajno osjetljivija metoda sa boljom stopom otkrivanja raka dojke u odnosu na zlatni standard, mamografiju. Korištenje ovih dviju metoda zajedno u probiru pokazalo je izvrsne rezultate koji potvrđuju važnost implementacije u kliničku praksu. Nedostatak kombinacije ABUS-a i mamografije je bio taj što je u velikom broju studija specifičnost bila niža u odnosu na samu mamografiju. U odnosu na DBT, ABUS je pokazao superiornije rezultate, osim u detekciji kalcifikacija. Iako je ABUS pokazao nešto lošije rezultate u usporedbi s MRI-om, jednostavnost uporabe i niska cijena čine ga alternativom MRI-u. Što se pak HHUS-a tiče, kao njegovu prednost u odnosu na ABUS pacijentice su navele manje bolan pregled i kraće trajanje, iako se on pokazao manje osjetljivijim i specifičnijim u odnosu na ABUS. Korištenje umjetne inteligencije danas postaje svakodnevnica, pa su tako razvijeni i posebni CAD softveri za ABUS kojima je svrha poboljšati stopu otkrivanja raka dojke i točnost radiologa. Korištenje CAD-a značajno je smanjilo vrijeme očitavanja slika te poboljšalo dijagnostičku točnost ABUS-a. Prema svim iznesenim podatcima, važnost ABUS uređaja u kliničkoj praksi je iznimno velika, a daljnim razvojem tehnologije i medicine, očekuje se njegova potpuna integracija u zdravstvene sustave diljem svijeta