Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning

Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are limited by the lack of image-specific adaptation and the lack of generalizability to previously unseen object classes. To address these problems, we propose a novel deep learning-based framework for interactive segmentation by incorporating CNNs into a bounding box and scribble-based segmentation pipeline. We propose image-specific fine-tuning to make a CNN model adaptive to a specific test image, which can be either unsupervised (without additional user interactions) or supervised (with additional scribbles). We also propose a weighted loss function considering network and interaction-based uncertainty for the fine-tuning. We applied this framework to two applications: 2D segmentation of multiple organs from fetal MR slices, where only two types of these organs were annotated for training; and 3D segmentation of brain tumor core (excluding edema) and whole brain tumor (including edema) from different MR sequences, where only tumor cores in one MR sequence were annotated for training. Experimental results show that 1) our model is more robust to segment previously unseen objects than state-of-the-art CNNs; 2) image-specific fine-tuning with the proposed weighted loss function significantly improves segmentation accuracy; and 3) our method leads to accurate results with fewer user interactions and less user time than traditional interactive segmentation methods.Comment: 11 pages, 11 figure

A Log-Euclidean and Total Variation based Variational Framework for Computational Sonography

We propose a spatial compounding technique and variational framework to improve 3D ultrasound image quality by compositing multiple ultrasound volumes acquired from different probe orientations. In the composite volume, instead of intensity values, we estimate a tensor at every voxel. The resultant tensor image encapsulates the directional information of the underlying imaging data and can be used to generate ultrasound volumes from arbitrary, potentially unseen, probe positions. Extending the work of Hennersperger et al., we introduce a log-Euclidean framework to ensure that the tensors are positive-definite, eventually ensuring non-negative images. Additionally, we regularise the underpinning ill-posed variational problem while preserving edge information by relying on a total variation penalisation of the tensor field in the log domain. We present results on in vivo human data to show the efficacy of the approach.Comment: SPIE Medical Imaging 201

Activating PIK3CA Mutations Induce an Epidermal Growth Factor Receptor (EGFR)/Extracellular Signal-regulated Kinase (ERK) Paracrine Signaling Axis in Basal-like Breast Cancer

Mutations in PIK3CA, the gene encoding the p110α catalytic subunit of phosphoinositide 3-kinase (PI3K) have been shown to transform human mammary epithelial cells (MECs). These mutations are present in all breast cancer subtypes, including basal-like breast cancer (BLBC). Using liquid chromatography-tandem mass spectrometry (LC-MS/MS), we identified 72 protein expression changes in human basal-like MECs with knock-in E545K or H1047R PIK3CA mutations versus isogenic MECs with wild-type PIK3CA. Several of these were secreted proteins, cell surface receptors or ECM interacting molecules and were required for growth of PIK3CA mutant cells as well as adjacent cells with wild-type PIK3CA. The proteins identified by MS were enriched among human BLBC cell lines and pointed to a PI3K-dependent amphiregulin/EGFR/ERK signaling axis that is activated in BLBC. Proteins induced by PIK3CA mutations correlated with EGFR signaling and reduced relapse-free survival in BLBC. Treatment with EGFR inhibitors reduced growth of PIK3CA mutant BLBC cell lines and murine mammary tumors driven by a PIK3CA mutant transgene, all together suggesting that PIK3CA mutations promote tumor growth in part by inducing protein changes that activate EGFR

Development of an effective mentorship program for preclinical medical student global health research training: An evaluation of a pilot mentorship program

As medical student interest in global health soars, so too does the desire by students to do research in international settings. However, very few medical students receive formal training in research skills. Mentorship is a key component of any research endeavour by students. The University of Texas Medical Branch focuses preclinical rotations on value-adding, mentored scholarly projects, developed with host site collaborators. The structure of this program allows preclinical students to engage meaningfully with international partners, who serve as research mentors. Mentorship is critical to successful development, implementation, and dissemination of scholarly projects. This paper describes a qualitative evaluation of a pilot mentorship program, which included semi-structured, one-on-one interviews with mentors and students who participated in the 2015 global health preclinical experience. Overall, mentors and students were satisfied with the mentorship experience. Challenges to mentoring were insufficient time and lack of student accountability to deadlines. Students reported satisfaction with the mentor relationship. The common theme from student interviews was the importance of communication. The better the communication, the better the experience

Using multiple mini interviews as a pre-screening tool for medical student candidates completing international health electives

There continues to be an increase in the number of learners who participate in international health electives (IHEs). However, not all learners enter IHEs with the same level of knowledge, attitude, and previous experience, which puts undue burden on host supervisors and poses risks to student and patient safety. The Multiple Mini-Interview (MMI) is a technique that has become a popular method for undergraduate and postgraduate-level health science admissions programs. This paper describes the MMI process used by our program to screen first-year medical students applying for pre-clinical IHEs. Two country-specific cases were developed to assess non-cognitive skills. One hundred percent (100%) of the students (n = 48) and interviewers (n = 10) who participated in MMIs completed anonymous surveys on their experience. The majority of students rated the scenarios as realistic (>90%); 96% found the MMI format fair and balanced; 96% of students felt that they were able to clearly articulate their thoughts; 75% of students stated that they had a general understanding of how the MMIs worked; only 33% of students would have preferred a traditional one-to-one interview. Feedback from both interviewers and students was positive toward the MMI experience, and no students were identified as unfit for participation. Ultimately, 43 students participated in pre-clinical IHEs in 2016. In this paper, we will outline our MMI process, detail shortcomings, and discuss our next steps to screen medical students for IHEs

Non-vascular interventional radiology in the paediatric alimentary tract

Paediatric interventional radiology is an evolving speciality which is able to offer numerous minimally invasive treatments for gastrointestinal tract pathologies. Here we describe interventions performed by paediatric interventional radiologists on the alimentary tract from the mouth to the rectum. The interventions include sclerotherapy, stricture management by dilation, stenting and adjunctive therapies such as Mitomycin C administration and enteral access for feeding, motility assessment and administration of enemas

An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI

High-resolution volume reconstruction from multiple motion-corrupted stacks of 2D slices plays an increasing role for fetal brain Magnetic Resonance Imaging (MRI) studies. Currently existing reconstruction methods are time-consuming and often require user interactions to localize and extract the brain from several stacks of 2D slices. We propose a fully automatic framework for fetal brain reconstruction that consists of four stages: 1) fetal brain localization based on a coarse segmentation by a Convolutional Neural Network (CNN), 2) fine segmentation by another CNN trained with a multi-scale loss function, 3) novel, single-parameter outlier-robust super-resolution reconstruction, and 4) fast and automatic high-resolution visualization in standard anatomical space suitable for pathological brains. We validated our framework with images from fetuses with normal brains and with variable degrees of ventriculomegaly associated with open spina bifida, a congenital malformation affecting also the brain. Experiments show that each step of our proposed pipeline outperforms state-of-the-art methods in both segmentation and reconstruction comparisons including expert-reader quality assessments. The reconstruction results of our proposed method compare favorably with those obtained by manual, labor-intensive brain segmentation, which unlocks the potential use of automatic fetal brain reconstruction studies in clinical practice